Wiki cleanup: error-doc removal, dedup merge, link normalization

10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-20 23:52:15 +09:00
parent 2a4a5046b6
commit f8b21af4be
2874 changed files with 15296 additions and 27684 deletions
@@ -166,10 +166,9 @@ function transformer<T extends ts.Node>(ctx: ts.TransformationContext) {
 **기본값**: Babel for JS/TS, LibCST for Python, tree-sitter for cross-language.

 ## 🔗 Graph
- 부모: [[Compiler-Theory]] · [[Static-Analysis]]
- 변형: [[Concrete-Syntax-Tree]] · [[Tree-Sitter]]
- 응용: [[Codemod]] · [[Linter]] · [[LLM-Code-Refactor]]
- Adjacent: [[Type-Inference]] · [[Symbol-Resolution]]
+- 부모: [[Static-Analysis]]
+- 변형: [[Concrete-Syntax-Tree]]
+- 응용: [[Linter]]

 ## 🤖 LLM 활용
 **언제**: large-scale code migration, repo-wide rename, API deprecation cleanup, structured code understanding for LLM context.
@@ -123,7 +123,7 @@ def adaptive_rag(query, user_profile):
 ## 🔗 Graph
 - 부모: [[Recommender-Systems]] · [[Active-Learning]]
 - 변형: [[Collaborative-Filtering]] · [[Multi-Armed-Bandit]]
- 응용: [[RAG]] · [[News-Feed-Ranking]] · [[Search-Reranking]]
+- 응용: [[RAG]]
 - Adjacent: [[Relevance-Feedback]] · [[Ranking-Algorithms]]

 ## 🤖 LLM 활용
@@ -139,8 +139,7 @@ const Result = z.discriminatedUnion("tag", [

 ## 🔗 Graph
 - 부모: [[Type-Theory]] · [[Algebraic-Data-Types]]
- 변형: [[Product-Type]] · [[GADT]] · [[Polymorphic-Variant]]
- 응용: [[Error-Handling]] · [[State-Machine]] · [[Parser-Combinator]]
+- 응용: [[Error-Handling]] · [[State-Machine]]
 - Adjacent: [[Pattern-Matching]] · [[Curry-Howard]]

 ## 🤖 LLM 활용
@@ -106,10 +106,7 @@ def reward_fn(images, prompts):
 **기본값**: ensemble (LAION + PickScore + human eval).

 ## 🔗 Graph
- 부모: [[Axiology]] · [[Philosophy-of-Art]]
- 변형: [[Computational-Aesthetics]] · [[Beauty-Metric]]
- 응용: [[Image-Generation-RLHF]] · [[Photo-Curation]] · [[UI-Design]]
- Adjacent: [[Reward-Modeling]] · [[Human-Preference]]
+- 부모: [[Axiology]]

 ## 🤖 LLM 활용
 **언제**: image / design quality reward, preference-tuned generation, large-scale curation.
@@ -98,10 +98,8 @@ def stress_proxy(rmssd_ms: float, baseline: float) -> float:
 **기본값**: AUDIT-C → if positive: brief intervention + naltrexone trial + referral.

 ## 🔗 Graph
- 부모: [[Addiction-Neuroscience]] · [[Mental-Health]]
- 변형: [[Substance-Use-Disorder]] · [[Behavioral-Addiction]]
- 응용: [[Digital-Therapeutics]] · [[Relapse-Prediction]] · [[Public-Health-Policy]]
- Adjacent: [[Dopamine]] · [[Burnout-Prevention]] · [[Cognitive-Behavioral-Therapy]]
+- 부모: [[Addiction-Neuroscience]]
+- Adjacent: [[Dopamine]] · [[Cognitive-Behavioral-Therapy]]

 ## 🤖 LLM 활용
 **언제**: care navigator, coping skill coaching, EMA prompting, literature synthesis for clinicians.
@@ -126,7 +126,7 @@ pstats.Stats('/tmp/prof').sort_stats('cumulative').print_stats(20)
 - 부모: [[Theoretical-Computer-Science]] · [[Theoretical-Computer-Science]]
 - 변형: [[BFS vs DFS]] · [[Dynamic-Programming]] · [[Greedy-Algorithms]]
 - 응용: [[Optimization-Algorithms]] · [[Combinatorial-Optimization]]
- Adjacent: [[Kolmogorov-Complexity]] · [[Information Theory]]
+- Adjacent: [[Kolmogorov-Complexity]] · [[Entropy in Information Theory|Information Theory]]

 ## 🤖 LLM 활용
 **언제**: 매 algorithm choice review, 매 scaling concern (n>10^5), 매 inference path optimization.
@@ -123,10 +123,10 @@ counter.compare_exchange(0, 1, Ordering::SeqCst, Ordering::SeqCst).ok();
 **기본값**: atomic decomposition + holistic review (avoid reductionist trap).

 ## 🔗 Graph
- 부모: [[Reductionism]] · [[Philosophy-of-Science]]
- 변형: [[Holism]] · [[Emergence]] · [[Logical-Atomism]]
- 응용: [[Atomic-Design]] · [[Atomic-CSS]] · [[Atomic-Commit]] · [[Zettelkasten]]
- Adjacent: [[Composition-over-Inheritance]] · [[Single-Responsibility-Principle]]
+- 부모: [[Reductionism]]
+- 변형: [[Emergence]] · [[Logical-Atomism]]
+- 응용: [[Atomic-Design]] · [[Atomic-CSS]]
+- Adjacent: [[Composition-over-Inheritance]] · [[Single Responsibility Principle (SRP)|Single-Responsibility-Principle]]

 ## 🤖 LLM 활용
 **언제**: design system creation, refactoring monolith, documentation structure, knowledge graph.
@@ -104,10 +104,9 @@ def coherence(events):
 **기본값**: Mem0 for runtime memory, LlamaIndex RAG for retrospective query.

 ## 🔗 Graph
- 부모: [[Narrative-Identity]] · [[Qualitative-Research]]
- 변형: [[Memoir]] · [[Autoethnography]] · [[Lifelog]]
- 응용: [[LLM-Personalization]] · [[Digital-Twin]] · [[Memory-Augmented-Agent]]
- Adjacent: [[Narrative-Therapy]] · [[Working-Memory]]
+- 변형: [[Memoir]] · [[Autoethnography]]
+- 응용: [[Digital-Twin]]
+- Adjacent: [[Working-Memory]]

 ## 🤖 LLM 활용
 **언제**: personal assistant memory, retrospective query over journal, coaching reflection prompts.
@@ -120,10 +120,10 @@ checks = [
 **기본값**: Analytic autoethnography with peer debrief + member check.

 ## 🔗 Graph
- 부모: [[Ethnography]] · [[Qualitative-Research]]
- 변형: [[Memoir]] · [[Reflective-Practice]] · [[Diary-Study]]
- 응용: [[UX-Research]] · [[AI-Ethics]] · [[Disability-Studies]]
- Adjacent: [[Autobiography]] · [[Grounded-Theory-Method]] · [[Thematic-Analysis]]
+- 부모: [[Ethnography]]
+- 변형: [[Memoir]]
+- 응용: [[AI-Ethics]]
+- Adjacent: [[Autobiography]] · [[Grounded-Theory-Method]]

 ## 🤖 LLM 활용
 **언제**: studying one's own use of tech / LLM, insider perspective on team or community, theoretical reflection.
@@ -127,10 +127,10 @@ class ConformityRecord:
 **기본값**: HITL + audit log + bias monitoring + appeal channel.

 ## 🔗 Graph
- 부모: [[Decision-Theory]] · [[ML-Systems]]
- 변형: [[Rule-Based-System]] · [[Agentic-AI]] · [[HITL]]
- 응용: [[Credit-Scoring]] · [[Content-Moderation]] · [[Hiring-Screening]]
- Adjacent: [[Algorithmic-Fairness]] · [[Model-Calibration]] · [[Audit-Logging]]
+- 부모: [[Decision-Theory]]
+- 변형: [[HITL]]
+- 응용: [[Content-Moderation]]
+- Adjacent: [[Algorithmic-Fairness]]

 ## 🤖 LLM 활용
 **언제**: drafting decision policy, reviewing audit logs for anomalies, generating explanation text (Art.22), bias test fixture generation.
@@ -137,8 +137,8 @@ osm_xml = polygons_to_osm(polygons, tag={"building": "yes"})

 ## 🔗 Graph
 - 부모: [[Procedural-Content-Generation]] · [[Computational-Geometry]]
- 변형: [[Wave-Function-Collapse]] · [[L-Systems]] · [[Cellular-Automata]]
- 응용: [[Game-Level-Design]] · [[GIS]] · [[Simulation-Environment]]
+- 변형: [[Cellular-Automata]]
+- 응용: [[GIS]]
 - Adjacent: [[Perlin-Noise]] · [[Diffusion-Models]] · [[Geographic-Information-Systems]]

 ## 🤖 LLM 활용
@@ -146,9 +146,8 @@ def transition(s, perception):

 ## 🔗 Graph
 - 부모: [[Robotics]] · [[Motion-Planning]] · [[Optimal-Control-Theory]]
- 변형: [[A-Star]] · [[RRT]] · [[Model-Predictive-Control]]
- 응용: [[Robotaxi]] · [[ADAS]] · [[Autonomous-Truck]]
- Adjacent: [[Kalman-Filter]] · [[Particle-Filter]] · [[ISO-26262]]
+- 응용: [[Robotaxi]]
+- Adjacent: [[Kalman-Filter-and-State-Tracking|Kalman-Filter]]

 ## 🤖 LLM 활용
 **언제**: scenario synthesis (corner cases), behavior reasoning prototype (LINGO-style), code generation for ROS / Apollo modules, log triage.
@@ -118,10 +118,9 @@ def is_pareto(point, all_points):
 **기본값**: pluralism + transparent weights + constitutional guardrails.

 ## 🔗 Graph
- 부모: [[Philosophy]] · [[Ethics]]
- 변형: [[Hedonism]] · [[Eudaimonism]] · [[Deontology]] · [[Virtue-Ethics]]
- 응용: [[AI-Alignment]] · [[Reward-Modeling]] · [[Cost-Benefit-Analysis]]
- Adjacent: [[Aesthetic-Value]] · [[Decision-Theory]] · [[Constitutional-AI]]
+- 부모: [[Philosophy]]
+- 응용: [[AI_Safety_and_Alignment|AI-Alignment]]
+- Adjacent: [[Aesthetic-Value]] · [[Decision-Theory]] · [[AI_Safety_and_Alignment|Constitutional-AI]]

 ## 🤖 LLM 활용
 **언제**: alignment policy drafting, principle articulation, value-laden decision review, ethical critique generation.
@@ -132,9 +132,9 @@ Suggest one tactic step. Output only the tactic.""")
 **기본값**: Lean 4 for new formalization, Coq for legacy verified systems.

 ## 🔗 Graph
- 부모: [[Mathematical-Logic]] · [[Foundations-of-Mathematics]]
- 변형: [[Type-Theory]] · [[Set-Theory]] · [[Natural-Deduction]]
- 응용: [[Formal-Verification]] · [[Theorem-Proving]] · [[Smart-Contract-Verification]]
+- 부모: [[Mathematical-Logic]]
+- 변형: [[Type-Theory]]
+- 응용: [[Formal-Verification]] · [[Theorem-Proving]]
 - Adjacent: [[Godel-s-Incompleteness-Theorems]] · [[Curry-Howard]] · [[Theoretical-Computer-Science]]

 ## 🤖 LLM 활용
@@ -160,10 +160,8 @@ def has_cycle(graph):
 **기본값**: shortest path 의 BFS, 매 structural analysis (topo, cycle, SCC) 의 DFS.

 ## 🔗 Graph
- 부모: [[Graph Theory]] · [[Search Algorithms]]
- 변형: [[Bidirectional Search]] · [[Iterative Deepening]] · [[Beam Search]]
- 응용: [[Dijkstra's Algorithm]] · [[A-Star Pathfinding]] · [[Topological Sort]] · [[Connected Components]]
- Adjacent: [[Tree Traversal]] · [[Tarjan SCC]] · [[Kosaraju Algorithm]]
+- 부모: [[Graph Theory]]
+- 응용: [[Dijkstra's Algorithm]] · [[Topological Sort]]

 ## 🤖 LLM 활용
 **언제**: 매 graph 문제 의 first-cut, 매 grid maze, 매 dependency resolution, 매 social network expansion.
@@ -148,9 +148,9 @@ winner = pop.run(eval_genomes, n=50)
 **기본값**: 매 continuous black-box 면 **CMA-ES**, 매 combinatorial 이면 **memetic GA**.

 ## 🔗 Graph
- 부모: [[Metaheuristics]] · [[Optimization]]
- 변형: [[Genetic-Algorithm]] · [[Particle-Swarm-Optimization]] · [[Ant-Colony-Optimization]] · [[Differential-Evolution]] · [[CMA-ES]] · [[NEAT]]
- 응용: [[Hyperparameter-Optimization]] · [[Neural-Architecture-Search]] · [[Robot-Control]] · [[Scheduling]]
+- 부모: [[Optimization]]
+- 변형: [[Genetic-Algorithm]] · [[CMA-ES]] · [[NEAT]]
+- 응용: [[Hyperparameters|Hyperparameter-Optimization]] · [[Neural-Architecture-Search-NAS|Neural-Architecture-Search]] · [[Scheduling]]
 - Adjacent: [[Bayesian-Optimization]] · [[Reinforcement-Learning]] · [[Simulated-Annealing]]

 ## 🤖 LLM 활용
@@ -113,10 +113,6 @@ print(shadow_radius_uas(6.5e9, 16800))  # M87* ~ 42 µas
 **기본값**: 매 astrophysics 면 **Kerr**, 매 CS / info-theoretic discussion 면 **Bekenstein–Hawking + holographic bound**.

 ## 🔗 Graph
- 부모: [[General-Relativity]] · [[Quantum-Field-Theory]]
- 변형: [[Schwarzschild-Metric]] · [[Kerr-Metric]] · [[Primordial-Black-Hole]]
- 응용: [[Holographic-Principle]] · [[AdS-CFT]] · [[Hawking-Radiation]] · [[Gravitational-Waves]]
- Adjacent: [[Information-Paradox]] · [[Page-Curve]] · [[ER-EPR]] · [[Computational-Limit]]

 ## 🤖 LLM 활용
 **언제**: 매 information-theoretic 한 entropy bound, 매 holographic / quantum gravity 의 thought experiment, 매 cosmology numerical estimation.
@@ -125,9 +125,8 @@ def shard(key, n_shards): return mmh3.hash(key) % n_shards
 **기본값**: 매 dynamic insert + lookup 의 negative-cache 에서는 **Bloom Filter (k = m/n · ln 2, p = 1%)**.

 ## 🔗 Graph
- 부모: [[Probabilistic-Data-Structures]] · [[Hash-Functions]]
- 변형: [[Counting-Bloom-Filter]] · [[Cuckoo-Filter]] · [[Quotient-Filter]] · [[XOR-Filter]]
- 응용: [[LSM-Tree]] · [[Inverted-Index]] · [[Web-Crawler]] · [[CDN-Cache]]
+- 변형: [[Cuckoo-Filter]]
+- 응용: [[LSM-Tree]]
 - Adjacent: [[HyperLogLog]] · [[Count-Min-Sketch]] · [[MinHash]]

 ## 🤖 LLM 활용
@@ -142,10 +142,6 @@ def bubble_sort_linked(head):
 **기본값**: 매 production 은 stdlib (Timsort/IntroSort) — 매 Bubble 은 교육 only.

 ## 🔗 Graph
- 부모: [[Sorting Algorithms]] · [[Comparison Sort]]
- 변형: [[Cocktail Shaker Sort]] · [[Odd-Even Sort]]
- 응용: [[Algorithm Education]] · [[Inversion Counting]]
- Adjacent: [[Insertion Sort]] · [[Selection Sort]] · [[Quicksort]] · [[Timsort]]

 ## 🤖 LLM 활용
 **언제**: 매 algorithm tutorials 의 explanation, 매 n<20 의 quick prototype, 매 inversions counting heuristic.
@@ -143,10 +143,7 @@ cbt_log = {
 **기본값**: 매 weekly MBI + daily sleep/load tracking + monthly psych check-in.

 ## 🔗 Graph
- 부모: [[Sports Psychology]] · [[Occupational Health]]
- 변형: [[Streamer Burnout]] · [[Coach Burnout]]
- 응용: [[Esports Team Management]] · [[Player Contracts]]
- Adjacent: [[Cognitive Neuroscience of Flow]] · [[Sleep Science]] · [[CBT]]
+- Adjacent: [[Cognitive Neuroscience of Flow]] · [[CBT]]

 ## 🤖 LLM 활용
 **언제**: 매 wellness check-in chatbot, 매 CBT thought-record assistance, 매 schedule optimization 의 load balancing.
@@ -123,10 +123,9 @@ focus:
 **기본값**: 매 explicit > implicit communication, 매 written > verbal context.

 ## 🔗 Graph
- 부모: [[Cognitive Theory]] · [[Neurodiversity]]
- 변형: [[Weak Central Coherence]] · [[Context Blindness (Vermeulen)]]
- 응용: [[Inclusive Engineering Practice]] · [[Specification Writing]]
- Adjacent: [[Autism Spectrum]] · [[Theory of Mind]] · [[Pragmatics]]
+- 부모: [[Neurodiversity]]
+- 변형: [[Weak Central Coherence]]
+- Adjacent: [[Theory of Mind]] · [[Pragmatics]]

 ## 🤖 LLM 활용
 **언제**: 매 spec disambiguation, 매 sarcasm/idiom annotation, 매 written-context expansion 의 ambiguous chat.
@@ -124,10 +124,8 @@ def takens_embed(series, m=3, tau=1):
 **기본값**: 매 nonlinear coupling 의 시스템 의 sensitivity analysis 우선.

 ## 🔗 Graph
- 부모: [[Dynamical Systems]] · [[Nonlinear Dynamics]]
- 변형: [[Lorenz Attractor]] · [[Logistic Map]] · [[Hénon Map]]
- 응용: [[Chaos Engineering]] · [[Backoff and Jitter]] · [[Stochastic Simulation]]
- Adjacent: [[Fractals]] · [[Strange Attractor]] · [[Bifurcation Theory]] · [[Lyapunov Exponent]]
+- 부모: [[Nonlinear Dynamics]]
+- 응용: [[Chaos Engineering]] · [[Stochastic Simulation]]

 ## 🤖 LLM 활용
 **언제**: 매 dynamical model 의 explanation, 매 simulation code generation, 매 chaos engineering policy authoring.
@@ -160,10 +160,7 @@ metrics_targets:
 **기본값**: GHG Protocol inventory + SBTi target + TCFD disclosure trifecta.

 ## 🔗 Graph
- 부모: [[ESG]] · [[Sustainability]] · [[Climate Policy]]
- 변형: [[Net Zero]] · [[Carbon Neutrality]] · [[Carbon Negative]]
- 응용: [[Green Software Engineering]] · [[Carbon-Aware Computing]] · [[Sustainable Datacenter]]
- Adjacent: [[Carbon Markets]] · [[Renewable Energy Certificates]] · [[Life Cycle Assessment]]
+- 부모: [[ESG]] · [[Sustainability]]

 ## 🤖 LLM 활용
 **언제**: 매 disclosure draft, 매 emission factor lookup, 매 MACC scenario synthesis, 매 carbon-aware code review.
@@ -144,10 +144,8 @@ class FlowSession:
 **기본값**: 매 challenge ≈ skill + 4%, 매 feedback < 200ms, 매 distraction-free 4hr blocks.

 ## 🔗 Graph
- 부모: [[Cognitive Neuroscience]] · [[Positive Psychology]]
- 변형: [[Group Flow]] · [[Microflow]] · [[Macroflow]]
 - 응용: [[Game Design]] · [[Esports Training]] · [[Burnout Prevention in Professional Gaming]]
- Adjacent: [[Default Mode Network]] · [[Dopamine]] · [[Attention]] · [[Csíkszentmihályi]]
+- Adjacent: [[Default Mode Network]] · [[Dopamine]] · [[Attention]]

 ## 🤖 LLM 활용
 **언제**: 매 difficulty curve design, 매 flow-friendly UX critique, 매 productivity ritual 의 personalization.
@@ -187,10 +187,8 @@ def set_cover_lp(sets, universe):
 **기본값**: 매 OR-Tools (CP-SAT) 의 free + production-grade.

 ## 🔗 Graph
- 부모: [[Optimization]] · [[Operations Research]] · [[Algorithms]]
- 변형: [[Linear Programming]] · [[Integer Programming]] · [[Constraint Programming]]
- 응용: [[Vehicle Routing]] · [[Job-Shop Scheduling]] · [[Bin Packing]] · [[Matching]]
- Adjacent: [[NP-Completeness]] · [[Approximation Algorithms]] · [[Metaheuristics]] · [[Neural CO]]
+- 부모: [[Optimization]] · [[Operations Research]]
+- 변형: [[Linear Programming]] · [[Integer Programming]]

 ## 🤖 LLM 활용
 **언제**: 매 problem-to-formulation translation, 매 OR-Tools/Gurobi code scaffold, 매 constraint reformulation 의 brainstorm.
@@ -115,10 +115,9 @@ theory_to_practice:
 **기본값**: 매 child topic 의 specific 의 navigate — 매 index 의 entry point only.

 ## 🔗 Graph
- 부모: [[10_Wiki Index]] · [[Programming Index]]
 - 자식: [[Bubble Sort]] · [[Combinatorial Optimization]] · [[Chaos Theory in Systems]] · [[Control Theory]] · [[Caetextia]] · [[Cognitive Neuroscience of Flow]] · [[Cross-Frequency Coupling (CFC)]] · [[Climate Change Mitigation Frameworks]] · [[Burnout Prevention in Professional Gaming]]
- Sibling 폴더: [[AI_and_ML]] · [[Architecture]] · [[Cognitive_Science_and_Neuroscience]] · [[DevOps_and_Security]]
- Adjacent: [[Mathematics]] · [[Information Theory]] · [[Logic]]
+- Sibling 폴더: [[Architecture]] · [[DevOps_and_Security]]
+- Adjacent: [[Entropy in Information Theory|Information Theory]] · [[Logic]]

 ## 🤖 LLM 활용
 **언제**: 매 navigation hub — 매 child topic 의 discovery.
@@ -173,10 +173,10 @@ def stability_margins(num, den):
 **기본값**: 매 PID + anti-windup — 매 production 의 80% case.

 ## 🔗 Graph
- 부모: [[Dynamical Systems]] · [[Cybernetics]]
- 변형: [[PID Controller]] · [[LQR]] · [[MPC]] · [[Adaptive Control]]
- 응용: [[Robotics]] · [[Autoscaling]] · [[TCP Congestion Control]] · [[Drone Flight Control]]
- Adjacent: [[Kalman Filter]] · [[Lyapunov Stability]] · [[Reinforcement Learning]] · [[Chaos Theory in Systems]]
+- 부모: [[Cybernetics Foundations|Cybernetics]]
+- 변형: [[MPC]]
+- 응용: [[Robotics]]
+- Adjacent: [[Reinforcement Learning]] · [[Chaos Theory in Systems]]

 ## 🤖 LLM 활용
 **언제**: 매 PID gain 의 starting point suggestion, 매 state-space derivation, 매 controller code scaffold.
@@ -162,10 +162,9 @@ def pac_zscore(signal, fs, phase_band, amp_band, n_perm=200):
 **기본값**: Tort 2010 MI + 200 surrogate permutations + cluster correction.

 ## 🔗 Graph
- 부모: [[Neural Oscillations]] · [[EEG Signal Processing]]
- 변형: [[Phase-Amplitude Coupling]] · [[Phase-Locking Value]] · [[n:m Coupling]]
- 응용: [[Brain-Computer Interface]] · [[Neurofeedback]] · [[Sleep Spindle Analysis]] · [[Cognitive Neuroscience of Flow]]
- Adjacent: [[Hilbert Transform]] · [[Wavelet Analysis]] · [[Working Memory]] · [[Theta Rhythm]] · [[Gamma Oscillations]]
+- 변형: [[Phase-Amplitude Coupling]]
+- 응용: [[Brain-Computer Interface]] · [[Cognitive Neuroscience of Flow]]
+- Adjacent: [[Working Memory]]

 ## 🤖 LLM 활용
 **언제**: 매 PAC pipeline scaffold, 매 metric choice 의 explanation, 매 surrogate-test 의 reasoning.
@@ -134,10 +134,10 @@ class ThompsonBandit:
 **기본값**: 매 Bayesian Expected Utility — 매 priors explicit + posterior update 가능.

 ## 🔗 Graph
- 부모: [[Probability Theory]] · [[Game Theory]]
- 변형: [[Bayesian-Decision-Theory]] · [[Prospect Theory]] · [[POMDP]]
- 응용: [[Reinforcement Learning]] · [[Multi-armed-Bandit-Problem]] · [[A_B Testing]]
- Adjacent: [[Optimal-Control-Theory]] · [[Information Theory]] · [[Risk-Management]]
+- 부모: [[Probability Theory]]
+- 변형: [[Prospect Theory]] · [[POMDP]]
+- 응용: [[Reinforcement Learning]] · [[Multi-armed-Bandit-Problem]]
+- Adjacent: [[Optimal-Control-Theory]] · [[Entropy in Information Theory|Information Theory]] · [[Risk_Management|Risk-Management]]

 ## 🤖 LLM 활용
 **언제**: 매 agent planner 의 tool selection, 매 RLHF reward design, 매 active learning data acquisition.
@@ -132,9 +132,7 @@ def test_replay_is_deterministic():

 ## 🔗 Graph
 - 부모: [[Theoretical-Computer-Science]] · [[Reproducibility]]
- 변형: [[Bit-Exact-Reproducibility]] · [[Statistical-Reproducibility]]
- 응용: [[ML Reproducibility]] · [[Blockchain Consensus]] · [[Lockstep-Networking]]
- Adjacent: [[Floating-Point-Arithmetic]] · [[Random Number Generation]] · [[Idempotency]]
+- Adjacent: [[Idempotency]]

 ## 🤖 LLM 활용
 **언제**: 매 evaluation harness, 매 regression test 의 ground truth, 매 paper code release.
@@ -156,9 +156,7 @@ path = nx.dijkstra_path(G, 'A', 'C')  # ['A','B','C'] (cost 4)

 ## 🔗 Graph
 - 부모: [[Graph Theory]] · [[Greedy-Algorithms]]
- 변형: [[A* Algorithm]] · [[Bidirectional Dijkstra]] · [[Bellman-Ford]]
- 응용: [[OSPF]] · [[Google Maps Routing]] · [[Network Flow]]
- Adjacent: [[Priority Queue]] · [[Fibonacci Heap]] · [[Topological Sort]]
+- Adjacent: [[Topological Sort]]

 ## 🤖 LLM 활용
 **언제**: 매 dependency resolution, 매 chain-of-thought planner 의 minimum-cost reasoning path.
@@ -149,9 +149,9 @@ etl()

 ## 🔗 Graph
 - 부모: [[Graph Theory]] · [[Topological Sort]]
- 변형: [[Build Graph]] · [[Reactive Signal Graph]] · [[Workflow DAG]]
+- 변형: [[Build Graph]]
 - 응용: [[Bazel]] · [[Airflow]] · [[Turborepo 환경 구성]] · [[Spark]]
- Adjacent: [[Critical-Path-Method]] · [[Job Scheduling]] · [[Incremental-Computation]]
+- Adjacent: [[Incremental-Computation]]

 ## 🤖 LLM 활용
 **언제**: 매 multi-step agent plan 의 dependency 표현, 매 RAG indexing pipeline orchestration.
@@ -135,8 +135,7 @@ def select_steady_state(states, entropy_production_fn):

 ## 🔗 Graph
 - 부모: [[Thermodynamics]] · [[Nonlinear-Dynamics]] · [[Complexity Science]]
- 변형: [[Self-Organized Criticality]] · [[Edge of Chaos]] · [[Active Matter]]
- 응용: [[Emergence-in-Systems]] · [[Reaction-Diffusion]] · [[Synergetics]]
+- 응용: [[Emergence-in-Systems]]
 - Adjacent: [[Entropy in Information Theory]] · [[Chaos-Theory in Systems]] · [[Free-Energy-Principle]]

 ## 🤖 LLM 활용
@@ -156,10 +156,9 @@ def tsp(dist: list[list[int]]) -> int:
 **기본값**: 매 first attempt 의 top-down + memo. 매 deep / huge 의 의 의 bottom-up.

 ## 🔗 Graph
- 부모: [[Algorithm Design]] · [[Optimization]]
- 변형: [[Memoization]] · [[Tabulation]] · [[Bitmask DP]] · [[Tree DP]] · [[Digit DP]]
- 응용: [[Knapsack Problem]] · [[LCS]] · [[Edit Distance]] · [[Bellman-Ford]] · [[Floyd-Warshall]] · [[Viterbi Algorithm]]
- Adjacent: [[Greedy Algorithms]] · [[Divide and Conquer]] · [[Branch and Bound]]
+- 부모: [[Optimization]]
+- 변형: [[Memoization]]
+- Adjacent: [[Greedy Algorithms]]

 ## 🤖 LLM 활용
 **언제**: 매 optimization problem 의 overlapping subproblem 의 detect, 매 string/sequence problem, 매 counting problem (combinatorial), 매 RL value function 의 의.
@@ -128,10 +128,7 @@ def growth_residual(eci, log_gdp_pc):
 **기본값**: 매 ECI for ranking + Fitness-Complexity for forecasting + Product Space for policy.

 ## 🔗 Graph
- 부모: [[Network Science]] · [[Econophysics]] · [[Complexity Science]]
- 변형: [[Fitness-Complexity]] · [[Genepy]] · [[Product Space]]
- 응용: [[Growth Forecasting]] · [[Industrial Policy]] · [[Smart Specialization]]
- Adjacent: [[Comparative Advantage]] · [[Bipartite Networks]] · [[Eigenvector Centrality]]
+- 부모: [[Complexity Science]]

 ## 🤖 LLM 활용
 **언제**: 매 country 분석 prompt 에 매 ECI 데이터 inject (Atlas of Economic Complexity API).
@@ -147,10 +147,10 @@ def layer_spectrum(W):
 **기본값**: `eigh` for symmetric; randomized SVD for high-dim ML.

 ## 🔗 Graph
- 부모: [[Linear-Algebra]] · [[Matrix-Theory]]
- 변형: [[SVD]] · [[Schur-Decomposition]] · [[QR-Decomposition]]
- 응용: [[PCA]] · [[PageRank]] · [[Spectral-Clustering]] · [[Kalman-Filter-and-State-Tracking]]
- Adjacent: [[Gimbals-and-Orientation]] · [[Hessian]] · [[Graph-Laplacian]]
+- 부모: [[Linear-Algebra-Foundations|Linear-Algebra]]
+- 변형: [[SVD]]
+- 응용: [[PCA]] · [[Spectral-Clustering]] · [[Kalman-Filter-and-State-Tracking]]
+- Adjacent: [[Gimbals-and-Orientation]]

 ## 🤖 LLM 활용
 **언제**: derive eigendecomposition, explain spectral properties, generate PCA/PageRank code, debug numerical issues (e.g. why eig gives complex for "symmetric" matrix).
@@ -114,10 +114,7 @@ def top1_share(values):
 **기본값**: Network analysis + Gini + power-law fit for empirical claims.

 ## 🔗 Graph
- 부모: [[Political-Theory]] · [[Sociology]]
- 변형: [[Pluralism]] · [[Marxism]] · [[Public-Choice]]
- 응용: [[AI-Governance]] · [[Platform-Economy]] · [[Liquid-Democracy]]
- Adjacent: [[Network-Centrality]] · [[Power-Law-Distribution]]
+- 응용: [[Liquid-Democracy]]

 ## 🤖 LLM 활용
 **언제**: literature synthesis on power dynamics, network construction from text data, generating hypotheses.
@@ -150,8 +150,8 @@ def emergence_index(micro, macro):
 ## 🔗 Graph
 - 부모: [[Complexity Science]] · [[Systems Theory]]
 - 변형: [[Weak-Emergence]] · [[Strong-Emergence]] · [[Self-Organization]]
- 응용: [[LLM-Emergent-Capabilities]] · [[Swarm-Intelligence]] · [[Market Microstructure]]
- Adjacent: [[Dissipative-Structures]] · [[Self-Organized-Criticality]] · [[Phase-Transitions]] · [[Emergence]]
+- 응용: [[Swarm_Intelligence|Swarm-Intelligence]]
+- Adjacent: [[Dissipative-Structures]] · [[Emergence]]

 ## 🤖 LLM 활용
 **언제**: 매 multi-agent prompt 의 collective behavior 분석, 매 capability scaling 예측.
@@ -131,10 +131,10 @@ def ising_step(spins, beta):
 **기본값**: weak emergence; demand operational definition + measurement.

 ## 🔗 Graph
- 부모: [[Complexity-Theory]] · [[Systems-Theory]]
+- 부모: [[Complexity_Theory|Complexity-Theory]] · [[Systems-Theory]]
 - 변형: [[Weak-Emergence]] · [[Strong-Emergence]] · [[Self-Organization]]
- 응용: [[Multi-Agent-Systems]] · [[Cellular-Automata]] · [[LLM-Scaling]]
- Adjacent: [[Phase-Transitions]] · [[Power-Law-Distribution]] · [[Global-Neuronal-Workspace]]
+- 응용: [[Multi-agent-System|Multi-Agent-Systems]] · [[Cellular-Automata]] · [[LLM-Scaling]]
+- Adjacent: [[Global-Neuronal-Workspace]]

 ## 🤖 LLM 활용
 **언제**: simulating CA / boids / Ising, explaining emergence intuitions, critiquing claims of "emergence" (Schaeffer-style metric scrutiny).
@@ -180,10 +180,8 @@ print(differential_entropy(samples_narrow))  # negative
 **기본값**: 매 ML loss 의 cross-entropy. 매 distribution distance 의 KL (asymmetric). 매 symmetric 가 필요하면 JS divergence.

 ## 🔗 Graph
- 부모: [[Information-Theory]] · [[Probability-Theory]]
- 변형: [[Differential-Entropy]] · [[Renyi-Entropy]] · [[Tsallis-Entropy]]
+- 부모: [[Information_Theory|Information-Theory]] · [[Probability-Theory]]
 - 응용: [[Cross-Entropy-Loss]] · [[KL-Divergence]] · [[Mutual-Information]] · [[Variational-Inference]]
- Adjacent: [[Shannon-Source-Coding]] · [[Channel-Capacity]] · [[Maximum-Entropy-Principle]] · [[Huffman-Coding]]

 ## 🤖 LLM 활용
 **언제**: 매 concept explanation, 매 derivation 의 walk-through, 매 ML loss function selection, 매 KL/cross-entropy 의 confused 시 disambiguation.
@@ -114,9 +114,8 @@ def expected_log_growth(p, weights, returns):
 **기본값**: assume non-ergodic in finance/biology/social; verify before using ensemble average.

 ## 🔗 Graph
- 부모: [[Probability-Theory]] · [[Dynamical-Systems]]
- 변형: [[Ergodicity-Economics]] · [[Birkhoff-Theorem]]
- 응용: [[Kelly-Criterion]] · [[MCMC]] · [[Reinforcement-Learning]]
+- 부모: [[Probability-Theory]]
+- 응용: [[MCMC]] · [[Reinforcement-Learning]]
 - Adjacent: [[Markov-Chains]] · [[Entropy-in-Information-Theory]]

 ## 🤖 LLM 활용
@@ -141,10 +141,9 @@ def redact_pii(s: str) -> str:
 **기본값**: 매 product discovery 의 **5-7 contextual inquiries (90 min each)** + open coding + affinity diagram.

 ## 🔗 Graph
- 부모: [[Qualitative-Research]] · [[Anthropology]] · [[HCI]]
- 변형: [[Contextual-Inquiry]] · [[Diary-Study]] · [[Auto-Ethnography]] · [[Digital-Ethnography]] · [[Netnography]]
- 응용: [[UX-Research]] · [[Product-Discovery]] · [[Service-Design]] · [[Jobs-To-Be-Done]]
- Adjacent: [[Grounded-Theory]] · [[Thematic-Analysis]] · [[Persona]] · [[Journey-Map]] · [[Thick-Description]]
+- 부모: [[HCI]]
+- 변형: [[Contextual-Inquiry]] · [[Auto-Ethnography]]
+- Adjacent: [[Grounded-Theory]]

 ## 🤖 LLM 활용
 **언제**: 매 transcript 의 first-pass open coding, 매 affinity cluster 의 candidate, 매 quote retrieval, 매 persona drafting.
@@ -171,10 +171,10 @@ for _ in range(200):
 **기본값**: 매 continuous → **CMA-ES**; 매 discrete → **memetic GA**; 매 LLM-era code search → **FunSearch-style**.

 ## 🔗 Graph
- 부모: [[Biological-Inspired-Algorithms]] · [[Metaheuristics]] · [[Optimization]]
- 변형: [[Genetic-Algorithm]] · [[Evolution-Strategy]] · [[Genetic-Programming]] · [[Differential-Evolution]] · [[CMA-ES]] · [[NEAT]] · [[MAP-Elites]]
- 응용: [[Neural-Architecture-Search]] · [[Hyperparameter-Optimization]] · [[Symbolic-Regression]] · [[Robot-Control]]
- Adjacent: [[Reinforcement-Learning]] · [[Bayesian-Optimization]] · [[Simulated-Annealing]] · [[FunSearch]]
+- 부모: [[Biological-Inspired-Algorithms]] · [[Optimization]]
+- 변형: [[Genetic-Algorithm]] · [[CMA-ES]] · [[NEAT]]
+- 응용: [[Neural-Architecture-Search-NAS|Neural-Architecture-Search]] · [[Hyperparameters|Hyperparameter-Optimization]]
+- Adjacent: [[Reinforcement-Learning]] · [[Bayesian-Optimization]] · [[Simulated-Annealing]]

 ## 🤖 LLM 활용
 **언제**: 매 black-box, multimodal, non-differentiable; 매 quality-diversity 가 필요; 매 program / prompt 의 search.
@@ -230,9 +230,7 @@ bgmm.fit(X)
 **기본값**: 매 GMM clustering 매 sklearn `GaussianMixture(n_init=10)`. 매 deep 매 VAE.

 ## 🔗 Graph
- 부모: [[Maximum-Likelihood-Estimation]] · [[Latent-Variable-Models]]
- 변형: [[Variational-EM]] · [[Stochastic-EM]] · [[MAP-EM]] · [[Hard-EM]]
- 응용: [[Gaussian-Mixture-Models]] · [[Hidden-Markov-Models]] · [[LDA-Topic-Modeling]] · [[VAE]]
+- 응용: [[VAE]]
 - Adjacent: [[Variational-Inference]] · [[Maximum-A-Posteriori]] · [[K-Means-Clustering-Foundations]] · [[Baum-Welch]]

 ## 🤖 LLM 활용
@@ -137,10 +137,10 @@ plt.xlabel("Occurrence"); plt.ylabel("Severity"); plt.grid()
 **기본값**: 매 AIAG-VDA 7-step + AP scoring (RPN deprecated).

 ## 🔗 Graph
- 부모: [[Reliability-Engineering]] · [[Risk-Management]] · [[Systems-Engineering]]
- 변형: [[DFMEA]] · [[PFMEA]] · [[FMECA]] · [[ML-FMEA]]
- 응용: [[ISO-26262]] · [[DO-178C]] · [[Six-Sigma]] · [[SRE]]
- Adjacent: [[Fault-Tree-Analysis]] · [[STPA]] · [[HAZOP]] · [[Bow-Tie-Analysis]] · [[Chaos-Engineering]]
+- 부모: [[Risk_Management|Risk-Management]]
+- 변형: [[DFMEA]] · [[PFMEA]] · [[FMECA]]
+- 응용: [[SRE]]
+- Adjacent: [[Chaos-Engineering]]

 ## 🤖 LLM 활용
 **언제**: 매 new system 의 risk register 를 brainstorm; 매 architecture review 의 failure-chain 의 enumeration; 매 ML deployment 의 pre-mortem.
@@ -161,10 +161,10 @@ class CPUAutoscaler:
 **기본값**: 매 SISO + slow plant → **PID with anti-windup + ZN-tuning**, 매 constraint-rich multi-input → **MPC**.

 ## 🔗 Graph
- 부모: [[Control-Theory]] · [[Cybernetics]] · [[Dynamical-Systems]]
- 변형: [[PID]] · [[LQR]] · [[MPC]] · [[Adaptive-Control]] · [[Robust-Control]] · [[Sliding-Mode-Control]]
- 응용: [[Autoscaler]] · [[Drone-Control]] · [[Process-Control]] · [[Robotics]] · [[Power-Electronics]]
- Adjacent: [[Feedback-Loops in Systems]] · [[Kalman-Filter]] · [[Reinforcement-Learning]] · [[Control-Barrier-Function]]
+- 부모: [[Control-Theory]] · [[Cybernetics Foundations|Cybernetics]]
+- 변형: [[PID]] · [[MPC]]
+- 응용: [[Robotics]]
+- Adjacent: [[Feedback-Loops in Systems]] · [[Kalman-Filter-and-State-Tracking|Kalman-Filter]] · [[Reinforcement-Learning]]

 ## 🤖 LLM 활용
 **언제**: 매 PID 의 tuning, 매 plant 의 transfer-function 의 derivation, 매 MPC objective 의 formulation, 매 stability 의 quick check.
@@ -145,10 +145,9 @@ delay: server provisioning ≈ 10 min  → oscillation risk.
 **기본값**: 매 design 의 첫 단계 = **CLD (Causal Loop Diagram)** + delay 표시 + leverage point 식별.

 ## 🔗 Graph
- 부모: [[Systems-Thinking]] · [[Cybernetics]] · [[Control-Theory]]
- 변형: [[Reinforcing-Loop]] · [[Balancing-Loop]] · [[Delayed-Feedback]] · [[Hysteresis]]
- 응용: [[Feedback-Control-Systems]] · [[Autoscaler]] · [[Reinforcement-Learning]] · [[Growth-Loop]] · [[Macro-Economics]]
- Adjacent: [[System-Dynamics]] · [[Causal-Loop-Diagram]] · [[Limits-to-Growth]] · [[Goodhart-Law]]
+- 부모: [[Systems_Thinking|Systems-Thinking]] · [[Cybernetics Foundations|Cybernetics]] · [[Control-Theory]]
+- 변형: [[Reinforcing-Loop]] · [[Balancing-Loop]]
+- 응용: [[Feedback-Control-Systems]] · [[Reinforcement-Learning]]

 ## 🤖 LLM 활용
 **언제**: 매 unintended consequence 의 prediction, 매 product growth 의 root-cause, 매 SRE incident 의 cascading 분석, 매 policy design 의 leverage point.
@@ -26,7 +26,7 @@ github_commit: pending

 ## 🔗 Graph
 - 부모: [[Flame-Graph]] (canonical)
- Adjacent: [[Profiling]] · [[Performance-Analysis]] · [[Speedscope]] · [[py-spy]] · [[perf]]
+- Adjacent: [[Profiling]] · [[Performance-Analysis]]

 ## 🕓 변경 이력
 | 날짜 | 변경 |
@@ -132,10 +132,8 @@ Map(layer)
 **기본값**: 매 modern 의 **GeoParquet + DuckDB-spatial + Lonboard** trio (laptop-scale), 매 server 의 **PostGIS**.

 ## 🔗 Graph
- 부모: [[Spatial-Analysis]] · [[Cartography]] · [[Databases]]
- 변형: [[Vector-GIS]] · [[Raster-GIS]] · [[3D-GIS]] · [[Spatiotemporal-GIS]]
- 응용: [[Routing]] · [[Remote-Sensing]] · [[Urban-Planning]] · [[Logistics]] · [[Climate-Modeling]]
- Adjacent: [[H3]] · [[S2]] · [[OpenStreetMap]] · [[STAC]] · [[GeoParquet]] · [[PostGIS]]
+- 응용: [[Routing]]
+- Adjacent: [[PostGIS]]

 ## 🤖 LLM 활용
 **언제**: 매 spatial-aware app 의 architecture, 매 CRS / projection 의 selection 의 explanation, 매 SQL 의 PostGIS 의 generation, 매 imagery analysis 의 prompting.
@@ -137,10 +137,9 @@ def quat_to_matrix(q):
 **기본값**: Quaternion internally, Euler at user boundaries.

 ## 🔗 Graph
- 부모: [[Linear-Algebra]] · [[3D-Geometry]]
- 변형: [[Quaternion]] · [[Euler-Angles]] · [[Rotation-Matrix]]
- 응용: [[Kalman-Filter-and-State-Tracking]] · [[VR-Head-Tracking]] · [[Robotics-IK]]
- Adjacent: [[Eigenvalues-and-Eigenvectors]] · [[Lie-Groups]]
+- 부모: [[Linear-Algebra-Foundations|Linear-Algebra]]
+- 응용: [[Kalman-Filter-and-State-Tracking]]
+- Adjacent: [[Eigenvalues-and-Eigenvectors]]

 ## 🤖 LLM 활용
 **언제**: orientation representation 의 conversion code 생성, gimbal lock debugging hints, sensor fusion math derivation.
@@ -157,10 +157,7 @@ theorem PA_consistent : PeanoArithmetic.Consistent := by
 **기본값**: 매 sufficiently expressive system 의 incompleteness 의 inherent — 매 "complete the system" 의 시도는 매 futile 하다 (e.g., Hilbert program 의 실패).

 ## 🔗 Graph
- 부모: [[Mathematical-Logic]] · [[Foundations-of-Mathematics]]
- 변형: [[Tarski-Undefinability]] · [[Löb's-Theorem]] · [[Rosser's-Theorem]]
- 응용: [[Halting-Problem]] · [[Computability-Theory]] · [[Provability-Logic]]
- Adjacent: [[Hilbert-Program]] · [[ZFC-Set-Theory]] · [[Lean-Theorem-Prover]] · [[Diagonalization]]
+- 부모: [[Mathematical-Logic]]

 ## 🤖 LLM 활용
 **언제**: 매 conceptual explanation, 매 historical context, 매 proof structure 의 high-level summary, 매 formal logic teaching.
@@ -16,7 +16,6 @@ github_commit: pending

 # Graph Theory

-> **이 문서는 [[Graph Theory Foundations]] 의 중복본입니다.** Canonical 문서로 redirect.

 ## 핵심 요약
 - Graph G = (V, E): vertex 의 set + edge 의 set.
@@ -25,8 +24,7 @@ github_commit: pending
 - Application: BFS/DFS, Dijkstra, MST, network analysis, social graph.

 ## 🔗 Graph
- 부모: [[Graph Theory Foundations]] (canonical)
- Adjacent: [[BFS vs DFS]] · [[Dijkstra's Algorithm]] · [[Network Analysis]]
+- Adjacent: [[BFS vs DFS]] · [[Dijkstra's Algorithm]]

 ## 🕓 변경 이력
 | 날짜 | 변경 |
@@ -230,10 +230,7 @@ def chaitin_register_allocation(interference_graph, num_registers):
 **기본값**: NetworkX `greedy_color(strategy='DSATUR')` — 매 quality/speed balance 우수.

 ## 🔗 Graph
- 부모: [[Graph-Theory]] · [[Combinatorial-Optimization]]
- 변형: [[Edge-Coloring]] · [[List-Coloring]] · [[Total-Coloring]]
- 응용: [[Register-Allocation]] · [[Scheduling-Algorithms]] · [[Constraint-Satisfaction]]
- Adjacent: [[NP-Completeness]] · [[Approximation-Algorithms]] · [[Tabu-Search]]
+- 부모: [[Graph_Theory|Graph-Theory]] · [[Combinatorial-Optimization]]

 ## 🤖 LLM 활용
 **언제**: 매 problem decomposition, 매 algorithm selection, 매 code skeleton 생성, 매 ILP formulation 자동화.
@@ -147,10 +147,9 @@ def set_cover(universe: set, sets: list[set]) -> list[int]:
 **기본값**: 매 problem 의 simple structure 의 greedy 의 first 시도. 매 counterexample 의 found → DP / B&B.

 ## 🔗 Graph
- 부모: [[Algorithm Design]] · [[Optimization]]
- 변형: [[Matroid Theory]] · [[Approximation Algorithms]]
- 응용: [[Kruskal MST]] · [[Prim MST]] · [[Dijkstra's Algorithm]] · [[Huffman Coding]] · [[Set Cover]] · [[Activity Selection]]
- Adjacent: [[Dynamic Programming]] · [[Branch and Bound]] · [[Linear Programming]]
+- 부모: [[Optimization]]
+- 응용: [[Dijkstra's Algorithm]]
+- Adjacent: [[Dynamic Programming]] · [[Linear Programming]]

 ## 🤖 LLM 활용
 **언제**: 매 simple optimization, 매 sorting + linear scan 의 의 의 의 의 의, 매 NP-hard 의 approximation 의 의, 매 streaming algorithm.
@@ -187,10 +187,9 @@ asymmetric cost structure 의 produce. 매 Theoretical sampling: 매 next,
 **기본값**: 매 modern (2026) 의 **Charmaz constructivist** — 매 reflexivity 의 honest, LLM-assistance OK 의 transparent.

 ## 🔗 Graph
- 부모: [[Qualitative-Research-Methods]] · [[Research-Methodology]]
+- 부모: [[Research-Methodology]]
 - 변형: [[Glaserian-GT]] · [[Straussian-GT]] · [[Constructivist-GT]]
- 응용: [[HCI-Research]] · [[Empirical-Software-Engineering]] · [[Nursing-Research]]
- Adjacent: [[Thematic-Analysis]] · [[Phenomenology]] · [[Ethnography]] · [[Theoretical-Sampling]]
+- Adjacent: [[Ethnography]]

 ## 🤖 LLM 활용
 **언제**: 매 first-pass open coding 의 acceleration, 매 memo 작성 의 brainstorm, 매 literature 의 sensitizing concepts 의 식별, 매 saturation 의 estimation.
@@ -142,9 +142,8 @@ report cdc -severity error
 **기본값**: UVM for blocks + formal for control + emulation for system.

 ## 🔗 Graph
- 부모: [[Formal-Methods]] · [[VLSI-Design]]
- 변형: [[Simulation-Based-Verification]] · [[Formal-Verification]] · [[Emulation]]
- 응용: [[RISC-V-Verification]] · [[AI-Accelerator-Verification]] · [[ISO-26262]]
+- 부모: [[Formal-Methods]]
+- 변형: [[Formal-Verification]]
 - Adjacent: [[Model-Checking]] · [[Theorem-Proving]]

 ## 🤖 LLM 활용
@@ -240,10 +240,8 @@ print(bf.contains("bob"))     # False (definitely) or True (false-positive)
 **기본값**: Rust 매 `std::collections::HashMap`, C++ 매 `absl::flat_hash_map`, Python 매 `dict`. 매 performance-critical 시 ahash/FxHash 으로 교체.

 ## 🔗 Graph
- 부모: [[Data-Structures]] · [[Algorithms]]
- 변형: [[Bloom-Filter]] · [[Cuckoo-Hashing]] · [[HyperLogLog]] · [[Consistent-Hashing]]
- 응용: [[Database-Index]] · [[LRU-Cache]] · [[Symbol-Table]] · [[Distributed-Hash-Tables]]
- Adjacent: [[SHA-256]] · [[BLAKE3]] · [[SipHash]] · [[xxHash]] · [[Robin-Hood-Hashing]]
+- 변형: [[Bloom-Filter]] · [[HyperLogLog]] · [[Consistent-Hashing]]
+- Adjacent: [[SHA-256]] · [[xxHash]]

 ## 🤖 LLM 활용
 **언제**: 매 hash function 선택 의 advice, 매 hash table 의 implementation 의 review, 매 collision 의 root cause 의 analysis.
@@ -212,10 +212,9 @@ print(f"Match: {np.mean(recalled == patterns[0]):.2%}")
 **기본값**: 매 conceptual 매 Hebb's rule, 매 ML practice 매 Oja's rule, 매 spiking simulation 매 STDP.

 ## 🔗 Graph
- 부모: [[Computational-Neuroscience]] · [[Synaptic-Plasticity]] · [[Learning-Theory]]
- 변형: [[Oja's-Rule]] · [[BCM-Rule]] · [[STDP]] · [[Anti-Hebbian-Learning]]
- 응용: [[Hopfield-Network]] · [[Self-Organizing-Maps]] · [[Associative-Memory]] · [[Predictive-Coding]]
- Adjacent: [[Long-Term-Potentiation]] · [[NMDA-Receptors]] · [[Free-Energy-Principle]] · [[Backpropagation]]
+- 부모: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Synaptic-Plasticity]]
+- 응용: [[Hopfield-Network]] · [[Associative-Memory]] · [[Predictive-Coding]]
+- Adjacent: [[Free-Energy-Principle]] · [[데이터_사이언스_및_ML_엔지니어링|Backpropagation]]

 ## 🤖 LLM 활용
 **언제**: 매 conceptual explanation, 매 history of computational neuroscience, 매 simple rule derivation.
@@ -134,7 +134,7 @@ model = lgb.train({'objective':'regression','num_leaves':31},
 - 부모: [[Operations-Research]] · [[Statistics]]
 - 변형: [[Algorithmic Trading]] · [[Market Making]]
 - 응용: [[Kalman-Filter-and-State-Tracking]] · [[Optimal-Control-Theory]]
- Adjacent: [[Stochastic-Processes]] · [[Signal-Processing-Foundations]]
+- Adjacent: [[Signal-Processing-Foundations]]

 ## 🤖 LLM 활용
 **언제**: research signal hypothesis 생성, 매 backtest code scaffolding, 매 strategy documentation.
@@ -127,9 +127,8 @@ const doubled = createMemo(() => count() * 2);

 ## 🔗 Graph
 - 부모: [[Theoretical-Computer-Science]] · [[Algorithm-Complexity-Big-O]]
- 변형: [[Memoization]] · [[Self-Adjusting-Computation]]
- 응용: [[Compiler-Architecture]] · [[Reactive-Programming]]
- Adjacent: [[Dynamic-Programming]] · [[Caching-Strategies]]
+- 변형: [[Memoization]]
+- Adjacent: [[Dynamic-Programming]]

 ## 🤖 LLM 활용
 **언제**: 매 build pipeline design, 매 query system architecture, 매 cache invalidation logic.
@@ -117,9 +117,9 @@ q_hat = np.quantile(calib_residuals, 0.95)

 ## 🔗 Graph
 - 부모: [[Statistics]] · [[Probability Theory]]
- 변형: [[Bayesian-Inference]] · [[Robust-Statistics]]
+- 변형: [[Bayesian-Inference]]
 - 응용: [[Statistical-Power]] · [[Multivariate-Analysis]]
- Adjacent: [[Inexact-Reasoning]] · [[Epistemology]]
+- Adjacent: [[Epistemology]]

 ## 🤖 LLM 활용
 **언제**: 매 study design review, 매 uncertainty communication, 매 robustness check 제안.
@@ -3,31 +3,25 @@ id: wiki-20260508-information-retrieval-ir--redir
 title: Information Retrieval (IR)
 category: 10_Wiki/Topics
 status: duplicate
-canonical_id: information-retrieval
-duplicate_of: "[[Information-Retrieval]]"
+canonical_id: wiki-2026-0508-information-retrieval-ir
+duplicate_of: "[[Information Retrieval (IR)]]"
 aliases: []
 source_trust_level: A
 confidence_score: 0.9
 verification_status: redirected
-tags: [duplicate, information-retrieval, search]
-last_reinforced: 2026-05-10
+tags: [duplicate]
+last_reinforced: 2026-05-20
 github_commit: pending
 ---

 # Information Retrieval (IR)

-> **이 문서는 [[Information-Retrieval]] 의 중복본입니다.** Canonical 문서로 redirect.
-
-## 핵심 요약 (specialization aspects)
- 매 acronym form (IR) — 매 canonical 매 full form.
- 매 BM25, dense vector (DPR, ColBERT), hybrid (RRF) 매 2026 standard.
+> **이 문서는 [[Information Retrieval (IR)]] 의 중복본입니다.** Canonical 문서로 redirect.

 ## 🔗 Graph
- 부모: [[Information-Retrieval]] (canonical)
- 관련: [[Information Retrieval Evaluation Metrics]] · [[Keyword Search]] · [[Ranking-Algorithms]]
+- 부모: [[Information Retrieval (IR)]] (canonical)

 ## 🕓 변경 이력
 | 날짜 | 변경 |
 |---|---|
-| 2026-05-08 | Phase 1 |
-| 2026-05-10 | 중복 처리 — canonical 문서로 redirect |
+| 2026-05-20 | 중복 병합 — canonical 문서로 redirect |
@@ -130,7 +130,7 @@ t, p = ttest_rel(ndcg_system_A, ndcg_system_B)
 - 부모: [[Information-Retrieval]] · [[Statistics]]
 - 변형: [[Ranking-Algorithms]] · [[Relevance-Feedback]]
 - 응용: [[Keyword Search]] · [[Knowledge Graph]]
- Adjacent: [[Statistical-Power]] · [[Cross-Validation]]
+- Adjacent: [[Statistical-Power]]

 ## 🤖 LLM 활용
 **언제**: 매 search system A/B 설계, 매 RAG eval pipeline, 매 metric trade-off 분석.
@@ -123,7 +123,7 @@ h = differential_entropy(samples)  # ≈ 0.5*log(2πe) ≈ 1.42 nat
 ## 🔗 Graph
 - 부모: [[Entropy in Information Theory]] · [[Probability Theory]]
 - 변형: [[Mutual-Information]] · [[Kullback-Leibler-Divergence]] · [[Kolmogorov-Complexity]]
- 응용: [[Information Theory]] · [[Information Retrieval (IR)]]
+- 응용: [[Entropy in Information Theory|Information Theory]] · [[Information Retrieval (IR)]]
 - Adjacent: [[Statistical-Power]] · [[Bayesian-Inference]]

 ## 🤖 LLM 활용
@@ -124,7 +124,7 @@ inner = quad(lambda t: f(t) * np.conj(g(t)), -np.pi, np.pi)[0]

 ## 🔗 Graph
 - 부모: [[Linear-Algebra-Foundations]] · [[Theoretical-Computer-Science]]
- 변형: [[Hilbert-Space]] · [[RKHS]]
+- 변형: [[Hilbert-Space]]
 - 응용: [[Similarity-Metrics]] · [[Singular-Value-Decomposition]] · [[PCA-and-Dimension-Reduction]]
 - Adjacent: [[Eigenvalues-and-Eigenvectors]] · [[Operator-Theory]]

@@ -131,10 +131,10 @@ graph TD
 **기본값**: 매 hypothesis tree (testable leaves) — 매 consulting/strategy 매 standard.

 ## 🔗 Graph
- 부모: [[Problem Solving]] · [[Strategic Thinking]]
- 변형: [[Logic Trees]] · [[Mental Models]]
+- 부모: [[Problem_Solving|Problem Solving]] · [[Strategic_Thinking|Strategic Thinking]]
+- 변형: [[Logic Trees]] · [[Mental_Models|Mental Models]]
 - 응용: [[Root-Cause-Analysis-RCA]] · [[Mutually Exclusive and Collectively Exhaustive (MECE)]] · [[Profitability Framework]]
- Adjacent: [[Decision Theory]] · [[Tree-of-Thoughts]]
+- Adjacent: [[Decision Theory]]

 ## 🤖 LLM 활용
 **언제**: 매 task decomposition, 매 root-cause investigation, 매 case-interview prep, 매 ToT/agent planning.
@@ -143,7 +143,7 @@ xs_sm, Ps_sm = rts_smoother(xs, Ps, F, Q)  # 매 future info 활용

 ## 🔗 Graph
 - 부모: [[Probability Theory]] · [[Optimal-Control-Theory]] · [[Linear-Algebra-Foundations]]
- 변형: [[Particle-Filter-Algorithms]] · [[Extended-Kalman-Filter]]
+- 변형: [[Particle-Filter-Algorithms]]
 - 응용: [[Autonomous-Vehicle-Path-Planning]] · [[High-Frequency-Trading-Models]]
 - Adjacent: [[Bayesian-Inference]] · [[Signal-Processing-Foundations]] · [[Gimbals-and-Orientation]]

@@ -120,10 +120,9 @@ sns.kdeplot(data=df, x="feature", hue="class", fill=True, common_norm=False)
 **기본값**: Silverman + Gaussian kernel, then validate.

 ## 🔗 Graph
- 부모: [[Non-Parametric-Statistics]] · [[Density-Estimation]]
- 변형: [[Histogram]] · [[Mean-Shift]] · [[Mixture-Models]]
- 응용: [[Anomaly-Detection]] · [[Visualization]]
- Adjacent: [[Bandwidth-Selection]] · [[Kernel-Methods]]
+- 부모: [[Density-Estimation]]
+- 응용: [[Anomaly-Detection]]
+- Adjacent: [[Kernel-Methods]]

 ## 🤖 LLM 활용
 **언제**: small/mid n, distribution shape 알 수 없을 때.
@@ -149,10 +149,10 @@ class InvertedIndex:
 **기본값**: BM25 + RRF fusion with dense retriever.

 ## 🔗 Graph
- 부모: [[Information-Retrieval]] · [[Search-Engines]]
- 변형: [[BM25]] · [[TF-IDF]] · [[Query-Likelihood]]
- 응용: [[RAG-Architecture]] · [[Hybrid-Search]] · [[Semantic-Search]]
- Adjacent: [[Inverted-Index]] · [[Tokenization]]
+- 부모: [[Information-Retrieval]]
+- 변형: [[BM25]] · [[TF-IDF]]
+- 응용: [[Hybrid-Search]] · [[Semantic Search|Semantic-Search]]
+- Adjacent: [[Tokenization]]

 ## 🤖 LLM 활용
 **언제**: exact-name retrieval, low-resource lang, long-tail terms, hybrid system.
@@ -136,10 +136,10 @@ class TransE(nn.Module):
 **기본값**: Neo4j + LLM extraction pipeline.

 ## 🔗 Graph
- 부모: [[Knowledge-Graph-Foundations]] · [[Graph-Theory]]
- 변형: [[GraphRAG]] · [[Entity-Linking]] · [[Ontology]]
- 응용: [[RAG-Architecture]] · [[Semantic-Search]] · [[Recommendation-Systems]]
- Adjacent: [[Vector-Database]] · [[Embeddings]]
+- 부모: [[Knowledge Graph|Knowledge-Graph-Foundations]] · [[Graph_Theory|Graph-Theory]]
+- 변형: [[GraphRAG]] · [[Ontology]]
+- 응용: [[Semantic Search|Semantic-Search]] · [[Recommendation-Systems]]
+- Adjacent: [[Embeddings]]

 ## 🤖 LLM 활용
 **언제**: factual grounding, multi-hop reasoning, entity disambiguation 필요 시.
@@ -2,152 +2,26 @@
 id: wiki-2026-0508-knowledge-graph-foundations
 title: Knowledge Graph Foundations
 category: 10_Wiki/Topics
-status: verified
-canonical_id: self
-aliases: [KG Foundations, Semantic Web Foundations]
-duplicate_of: none
+status: duplicate
+canonical_id: wiki-2026-0508-knowledge-graph
+duplicate_of: "[[Knowledge Graph]]"
+aliases: []
 source_trust_level: A
 confidence_score: 0.9
-verification_status: applied
-tags: [knowledge-graph, foundations, rdf, ontology]
-raw_sources: []
-last_reinforced: 2026-05-10
+verification_status: redirected
+tags: [duplicate]
+last_reinforced: 2026-05-20
 github_commit: pending
-tech_stack:
-  language: python
-  framework: rdflib, owlready2
 ---

 # Knowledge Graph Foundations

-## 매 한 줄
-> **"매 RDF + ontology + reasoning 의 stack"**. KG Foundations 는 W3C Semantic Web stack (RDF, RDFS, OWL, SPARQL) 의 formal foundation 과 graph data model theory 를 다룬다. 2026 modern KG 는 이 foundation 위에 LLM extraction 과 vector embeddings 을 hybrid 로 결합.
-
-## 매 핵심
-
-### 매 Data Model 계층
- **RDF**: triple — (s, p, o), URIs 로 entity 식별
- **RDFS**: schema layer — class, subClassOf, domain, range
- **OWL**: full ontology — equivalence, disjointness, cardinality
- **SHACL**: constraint validation
-
-### 매 Reasoning
- forward chaining: rules → derived triples
- backward chaining: query-driven inference
- DL reasoning (Description Logic): subsumption, consistency
-
-### 매 응용
-1. DBpedia, Wikidata: 매 large-scale public KG.
-2. Schema.org: 매 web markup ontology.
-3. Bio2RDF: 매 life sciences integration.
-
-## 💻 패턴
-
-### RDF graph build
-```python
-from rdflib import Graph, Namespace, URIRef, Literal, RDF, RDFS
-
-EX = Namespace("http://example.org/")
-g = Graph()
-g.bind("ex", EX)
-
-g.add((EX.Claude, RDF.type, EX.LLM))
-g.add((EX.LLM, RDFS.subClassOf, EX.AISystem))
-g.add((EX.Claude, EX.creator, EX.Anthropic))
-g.serialize("kg.ttl", format="turtle")
-```
-
-### OWL ontology with owlready2
-```python
-from owlready2 import *
-
-onto = get_ontology("http://example.org/onto.owl")
-with onto:
-    class AISystem(Thing): pass
-    class LLM(AISystem): pass
-    class Organization(Thing): pass
-    class creator(ObjectProperty):
-        domain = [AISystem]
-        range = [Organization]
-
-claude = LLM("Claude")
-anthropic = Organization("Anthropic")
-claude.creator.append(anthropic)
-sync_reasoner()  # HermiT reasoner
-```
-
-### SPARQL federated query
-```python
-from SPARQLWrapper import SPARQLWrapper, JSON
-
-s = SPARQLWrapper("https://query.wikidata.org/sparql")
-s.setQuery("""
-SELECT ?company ?ceo WHERE {
-    ?company wdt:P31 wd:Q4830453 .  # company
-    ?company wdt:P169 ?ceo .         # CEO
-} LIMIT 10
-""")
-s.setReturnFormat(JSON)
-print(s.query().convert())
-```
-
-### SHACL validation
-```python
-from pyshacl import validate
-conforms, _, report = validate(
-    data_graph=g,
-    shacl_graph="shapes.ttl",
-    inference="rdfs",
-)
-```
-
-### Forward chaining inference
-```python
-def forward_chain(triples, rules):
-    derived = set(triples)
-    changed = True
-    while changed:
-        changed = False
-        for rule in rules:
-            new = rule.apply(derived)
-            if new - derived:
-                derived |= new
-                changed = True
-    return derived
-```
-
-## 매 결정 기준
-| 상황 | Stack |
-|---|---|
-| public LOD | RDF + SPARQL |
-| domain ontology, reasoning | OWL + HermiT/Pellet |
-| flexible schema | LPG (Neo4j) |
-| validation | SHACL |
-| LLM hybrid | RDF + embeddings |
-
-**기본값**: RDF + SPARQL + SHACL.
+> **이 문서는 [[Knowledge Graph]] 의 중복본입니다.** Canonical 문서로 redirect.

 ## 🔗 Graph
- 부모: [[Graph-Theory]] · [[Logic]]
- 변형: [[RDF]] · [[OWL]] · [[Description-Logic]]
- 응용: [[Knowledge Graph]] · [[Semantic-Web]] · [[Wikidata]]
- Adjacent: [[Ontology]] · [[Reasoning-Systems]]
+- 부모: [[Knowledge Graph]] (canonical)

-## 🤖 LLM 활용
-**언제**: formal semantics, reasoning, standards-compliant interop 필요.
-**언제 X**: ad-hoc agent memory — 매 simple key-value 면 충분.
-
-## ❌ 안티패턴
- **OWL overengineering**: full DL reasoner 가 필요없는데 OWL Full 사용 → undecidable.
- **URI minting chaos**: 매 새 entity 마다 random URI → linkability 손실.
- **Closed-world assumption**: RDF 는 OWA — "absent ≠ false".
-
-## 🧪 검증 / 중복
- Verified (W3C RDF 1.1 spec, OWL 2 spec, Hitzler 2009 textbook).
- 신뢰도 A.
-
-## 🕓 Changelog
+## 🕓 변경 이력
 | 날짜 | 변경 |
 |---|---|
-| 2026-05-08 | Phase 1 |
-| 2026-05-10 | Manual cleanup — RDF/OWL/SPARQL stack, reasoning, validation |
+| 2026-05-20 | 중복 병합 — canonical 문서로 redirect |
@@ -125,10 +125,10 @@ restaurant_frame = {
 **기본값**: Hierarchy + tags + wikilinks (hybrid).

 ## 🔗 Graph
- 부모: [[Information-Architecture]] · [[Knowledge-Representation]]
- 변형: [[Taxonomy]] · [[Ontology]] · [[Folksonomy]]
- 응용: [[Knowledge Graph]] · [[Wiki-Systems]] · [[RAG-Architecture]]
- Adjacent: [[Zettelkasten]] · [[Personal-Knowledge-Management]]
+- 부모: [[Knowledge-Representation]]
+- 변형: [[Ontology]]
+- 응용: [[Knowledge Graph]]
+- Adjacent: [[Personal-Knowledge-Management]]

 ## 🤖 LLM 활용
 **언제**: agent memory design, RAG indexing strategy, large doc collection 정리.
@@ -118,10 +118,10 @@ def is_likely_random(x: bytes, threshold=0.95) -> bool:
 **기본값**: LZMA approximation for compute, MDL for principled selection.

 ## 🔗 Graph
- 부모: [[Information-Theory]] · [[Computability-Theory]]
- 변형: [[Algorithmic-Probability]] · [[Levin-Complexity]] · [[MDL]]
- 응용: [[Compression]] · [[AIXI]] · [[Generalization-Bounds]]
- Adjacent: [[Shannon-Entropy]] · [[Chaitin-Omega]]
+- 부모: [[Information_Theory|Information-Theory]]
+- 변형: [[MDL]]
+- 응용: [[Compression]]
+- Adjacent: [[Shannon-Entropy]]

 ## 🤖 LLM 활용
 **언제**: model complexity reasoning, compression-as-intelligence arguments, generalization theory.
@@ -115,9 +115,8 @@ def ppo_kl_penalty(logp_new, logp_ref, beta=0.05):
 **기본값**: 매 problem 따라 — VAE 면 reverse, EP 면 forward.

 ## 🔗 Graph
- 부모: [[Information-Theory]] · [[Divergences]]
- 변형: [[Jensen-Shannon-Divergence]] · [[Wasserstein-Distance]] · [[f-Divergence]]
- 응용: [[VAE]] · [[RLHF]] · [[Knowledge-Distillation]] · [[Variational-Inference]]
+- 부모: [[Information_Theory|Information-Theory]]
+- 응용: [[VAE]] · [[RLHF]] · [[LLM_Optimization_and_Deployment_Strategies|Knowledge-Distillation]] · [[Variational-Inference]]
 - Adjacent: [[Cross-Entropy]] · [[Mutual-Information]]

 ## 🤖 LLM 활용
@@ -128,10 +128,8 @@ def augmented_lagrangian(f, g, x0, mu0=1.0, rho=1.5, iters=20):
 **기본값**: CVXPY for convex, scipy SLSQP otherwise.

 ## 🔗 Graph
- 부모: [[Optimization]] · [[Calculus]]
- 변형: [[KKT-Conditions]] · [[Duality]] · [[ADMM]]
- 응용: [[SVM]] · [[Constrained-RL]] · [[Regularization]]
- Adjacent: [[Convex-Optimization]] · [[Penalty-Methods]]
+- 부모: [[Optimization]]
+- 응용: [[SVM]] · [[L1-and-L2-Regularization|Regularization]]

 ## 🤖 LLM 활용
 **언제**: deriving SVM/RLHF math, constrained policy optimization, MLE w/ constraints.
@@ -99,9 +99,8 @@ def rls_update(P, beta, x, y, lam=0.99):

 ## 🔗 Graph
 - 부모: [[Linear-Algebra-Foundations]] · [[Optimization]]
- 변형: [[Ridge-Regression]] · [[Lasso]] · [[Total-Least-Squares]]
+- 변형: [[Ridge-Regression]] · [[Lasso]]
 - 응용: [[Kalman-Filter-and-State-Tracking]] · [[Linear-Regression]] · [[Bundle-Adjustment]]
- Adjacent: [[Maximum-Likelihood-Estimation]] · [[QR-Decomposition]]

 ## 🤖 LLM 활용
 **언제**: Linear/affine model fit, baseline regression, calibration curves.
@@ -137,10 +137,8 @@ def attention(Q, K, V, mask=None):
 **기본값**: SVD when in doubt — most stable, universal.

 ## 🔗 Graph
- 부모: [[Mathematics]] · [[Vector-Space]]
- 변형: [[SVD]] · [[Eigendecomposition]] · [[Tensor-Algebra]]
+- 변형: [[SVD]] · [[Eigendecomposition]]
 - 응용: [[PCA]] · [[Attention-Mechanism]] · [[Linear-Regression]]
- Adjacent: [[Numerical-Methods]] · [[Convex-Optimization]]

 ## 🤖 LLM 활용
 **언제**: 매 ML 의 derivation, debugging matrix shapes, performance reasoning.
@@ -200,10 +200,9 @@ def is_balanced(node) -> bool:
 **기본값**: list 의 X — dynamic array 의 의. tree 의 specific structure (BST sorted, heap priority, n-ary hierarchy) 의 의.

 ## 🔗 Graph
- 부모: [[Data Structures]] · [[Pointer Structures]]
- 변형: [[Doubly Linked List]] · [[Skip List]] · [[Binary Tree]] · [[N-ary Tree]] · [[BST]]
- 응용: [[LRU Cache]] · [[AST Abstract Syntax Tree]] · [[DOM Tree]] · [[Filesystem Tree]]
- Adjacent: [[Hash Functions and Maps]] · [[B-Tree]] · [[Heap Data Structure]] · [[Trie]]
+- 변형: [[Skip List]]
+- 응용: [[AST Abstract Syntax Tree]] · [[DOM Tree]]
+- Adjacent: [[Hash Functions and Maps]] · [[B-Tree]] · [[Trie]]

 ## 🤖 LLM 활용
 **언제**: 매 hierarchy 의 model (AST, DOM, filesystem), 매 LRU 의 doubly linked, 매 BST 의 sorted lookup, 매 trie 의 prefix.
@@ -140,10 +140,10 @@ def banded_lsh(signatures, k_per_band, L_bands):
 **기본값**: HNSW for general ANN (faster); LSH for dedup, streaming, external memory, exact-recall guarantee.

 ## 🔗 Graph
- 부모: [[Hashing]] · [[Approximate-Nearest-Neighbor]]
- 변형: [[MinHash]] · [[SimHash]] · [[Cross-Polytope-LSH]]
- 응용: [[Deduplication]] · [[Entity-Resolution]] · [[Plagiarism-Detection]]
- Adjacent: [[HNSW]] · [[Vector-Database]] · [[Faiss]]
+- 부모: [[Approximate-Nearest-Neighbor]]
+- 변형: [[MinHash]] · [[SimHash]]
+- 응용: [[Deduplication]]
+- Adjacent: [[HNSW]] · [[Faiss]]

 ## 🤖 LLM 활용
 **언제**: massive corpus dedup (e.g. pretraining cleanup), candidate blocking, streaming.
@@ -2,135 +2,26 @@
 id: wiki-2026-0508-locality-sensitive-hashing
 title: Locality Sensitive Hashing
 category: 10_Wiki/Topics
-status: verified
-canonical_id: self
-aliases: [LSH, Locality-Sensitive-Hashing, MinHash, SimHash]
-duplicate_of: none
+status: duplicate
+canonical_id: wiki-2026-0508-locality-sensitive-hashing-lsh
+duplicate_of: "[[Locality-Sensitive Hashing (LSH)]]"
+aliases: []
 source_trust_level: A
-confidence_score: 0.93
-verification_status: applied
-tags: [hashing, ann, similarity-search, embeddings, retrieval]
-raw_sources: []
-last_reinforced: 2026-05-10
+confidence_score: 0.9
+verification_status: redirected
+tags: [duplicate]
+last_reinforced: 2026-05-20
 github_commit: pending
-tech_stack:
-  language: python
-  framework: datasketch-faiss
 ---

 # Locality Sensitive Hashing

-## 매 한 줄
-> **"매 가까운 점은 매 같은 bucket에 떨어진다"**. LSH는 매 metric similarity를 hash collision probability로 변환하여 매 sub-linear ANN(approximate nearest neighbor) search를 가능케 한다. 2026 vector DB 시대에 IVF-PQ·HNSW에 밀렸지만, 매 streaming dedup·document near-dup detection에서 매 dominant.
-
-## 매 핵심
-
-### 매 family들
- **MinHash (Jaccard)**: 매 set similarity — shingled documents.
- **SimHash (cosine)**: 매 random hyperplane projection.
- **p-stable LSH (L2)**: 매 random Gaussian projection + bucketing.
- **Cross-polytope LSH**: 매 angular distance, 매 unit sphere 위.
-
-### 매 amplification
- AND-construction: 매 k hashes 모두 일치 → false positive ↓.
- OR-construction: 매 L tables 중 하나라도 collision → recall ↑.
- (k, L) tuning이 매 핵심.
-
-### 매 응용
-1. Plagiarism / near-duplicate detection (web, code).
-2. Streaming deduplication (logs, training data).
-3. Genomic sequence matching.
-4. Pre-filter for vector DBs at billion scale.
-
-## 💻 패턴
-
-### MinHash with datasketch
-```python
-from datasketch import MinHash, MinHashLSH
-def shingles(text, k=3):
-    return {text[i:i+k] for i in range(len(text)-k+1)}
-m = MinHash(num_perm=128)
-for s in shingles(doc):
-    m.update(s.encode())
-lsh = MinHashLSH(threshold=0.7, num_perm=128)
-lsh.insert("doc1", m)
-matches = lsh.query(m_query)
-```
-
-### SimHash (64-bit)
-```python
-import numpy as np
-def simhash(features, bits=64):
-    v = np.zeros(bits)
-    for f, w in features:
-        h = hash(f)
-        for i in range(bits):
-            v[i] += w if (h >> i) & 1 else -w
-    return sum(1 << i for i in range(bits) if v[i] > 0)
-```
-
-### Random projection LSH (cosine)
-```python
-class CosineLSH:
-    def __init__(self, dim, n_planes=16):
-        self.planes = np.random.randn(n_planes, dim)
-    def hash(self, x):
-        return tuple((self.planes @ x > 0).astype(int))
-```
-
-### p-stable LSH (Euclidean)
-```python
-def l2_hash(x, a, b, w):
-    return int((a @ x + b) // w)
-# a ~ N(0, I), b ~ U(0, w)
-```
-
-### Banding for MinHash
-```python
-def bands(sig, b, r):
-    return [tuple(sig[i*r:(i+1)*r]) for i in range(b)]
-# threshold ~ (1/b)^(1/r)
-```
-
-### FAISS LSH index (for L2)
-```python
-import faiss
-index = faiss.IndexLSH(dim, n_bits=256)
-index.add(X)
-D, I = index.search(query, k=10)
-```
-
-## 매 결정 기준
-| Metric | LSH family |
-|---|---|
-| Jaccard | MinHash |
-| Cosine | SimHash / random hyperplane |
-| Euclidean | p-stable / IndexLSH |
-| Hamming | Bit sampling |
-
-**기본값**: HNSW 우선, billion-scale dedup만 LSH.
+> **이 문서는 [[Locality-Sensitive Hashing (LSH)]] 의 중복본입니다.** Canonical 문서로 redirect.

 ## 🔗 Graph
- 부모: [[Hash-Functions-and-Maps]] · [[Approximate-Nearest-Neighbor]]
- 변형: [[MinHash]] · [[SimHash]] · [[Cross-Polytope-LSH]]
- 응용: [[Near-Duplicate-Detection]] · [[Vector-Database]] · [[Streaming-Dedup]]
- Adjacent: [[HNSW]] · [[Product-Quantization]] · [[Bloom-Filters in Search]]
+- 부모: [[Locality-Sensitive Hashing (LSH)]] (canonical)

-## 🤖 LLM 활용
-**언제**: Billion-scale dedup, Jaccard-based near-dup detection, streaming pipeline.
-**언제 X**: High-recall ANN with dense embeddings (use HNSW/IVF-PQ).
-
-## ❌ 안티패턴
- **단일 hash table**: recall 낮음 — 매 L tables 필수.
- **k 너무 큼**: false neg 폭증.
- **Dense embedding에 MinHash**: 매 잘못된 family 선택.
-
-## 🧪 검증 / 중복
- Verified (Indyk & Motwani 1998; Mining of Massive Datasets ch.3).
- 신뢰도 A.
-
-## 🕓 Changelog
+## 🕓 변경 이력
 | 날짜 | 변경 |
 |---|---|
-| 2026-05-08 | Phase 1 |
-| 2026-05-10 | Manual cleanup — LSH families + datasketch/FAISS examples |
+| 2026-05-20 | 중복 병합 — canonical 문서로 redirect |
@@ -111,10 +111,10 @@ def tree_to_tasks(node, owner=None):
 **기본값**: Hypothesis-driven Why-tree (depth 3-4).

 ## 🔗 Graph
- 부모: [[Mental Models]] · [[Mutually Exclusive and Collectively Exhaustive (MECE)]]
- 변형: [[Issue Tree]] · [[Decision Theory]] · [[Fault-Tree-Analysis]]
- 응용: [[Root-Cause-Analysis]] · [[LLM-Agent-Planning]] · [[5-Whys]]
- Adjacent: [[Pyramid-Principle]] · [[Hypothesis-Driven-Development]]
+- 부모: [[Mental_Models|Mental Models]] · [[Mutually Exclusive and Collectively Exhaustive (MECE)]]
+- 변형: [[Issue Tree]] · [[Decision Theory]]
+- 응용: [[Root-Cause-Analysis]] · [[5-Whys]]
+- Adjacent: [[Pyramid-Principle]]

 ## 🤖 LLM 활용
 **언제**: Open-ended problem decomposition, agent task planning, structured analysis.
@@ -137,10 +137,7 @@ def L10h_hours(L10_Mrev, n_rpm):
 **기본값**: 매 industrial machinery → **mineral/PAO oil + ZDDP additive + ISO VG selected by viscosity-temp/load**, 매 critical → **online oil + vibration CBM**.

 ## 🔗 Graph
- 부모: [[Tribology]] · [[Mechanical-Engineering]] · [[Materials-Science]]
- 변형: [[Hydrodynamic-Lubrication]] · [[EHL]] · [[Boundary-Lubrication]] · [[Solid-Lubricant]] · [[Grease]]
- 응용: [[Bearing]] · [[Gearbox]] · [[Internal-Combustion-Engine]] · [[Wind-Turbine]] · [[Hard-Disk-Drive]]
- Adjacent: [[Reynolds-Equation]] · [[Stribeck-Curve]] · [[Condition-Monitoring]] · [[Wear]] · [[Friction]]
+- 부모: [[Tribology]]

 ## 🤖 LLM 활용
 **언제**: 매 lubricant 의 selection table 의 first cut, 매 viscosity-temp curve 의 fitting, 매 oil-analysis report 의 interpretation, 매 CBM model architecture 의 prototyping.
@@ -114,8 +114,7 @@ solver.set_initial_guess(shift(u_prev))

 ## 🔗 Graph
 - 부모: [[Optimal-Control-Theory]] · [[Optimization]]
- 변형: [[Nonlinear-MPC]] · [[Robust-MPC]] · [[Stochastic-MPC]]
- 응용: [[Autonomous-Vehicle-Path-Planning]] · [[Quadrotor-Control]] · [[Humanoid-Robot]]
+- 응용: [[Autonomous-Vehicle-Path-Planning]]
 - Adjacent: [[Reinforcement-Learning]] · [[Kalman-Filter-and-State-Tracking]] · [[Feedback-Control-Systems]]

 ## 🤖 LLM 활용
@@ -111,10 +111,9 @@ def mc(key, n):
 **기본값**: Vanilla MC + control variates (low complexity, low variance).

 ## 🔗 Graph
- 부모: [[Numerical-Integration]] · [[Statistics]]
- 변형: [[Importance-Sampling]] · [[MCMC]] · [[Quasi-Monte-Carlo]]
- 응용: [[Bayesian-Inference]] · [[Path-Tracing]] · [[RLHF]]
- Adjacent: [[Variance-Reduction]] · [[Stratified-Sampling]]
+- 부모: [[Statistics]]
+- 변형: [[MCMC]]
+- 응용: [[Bayesian-Inference]] · [[RLHF]]

 ## 🤖 LLM 활용
 **언제**: High-dim integration, expectation under intractable distribution, simulation.
@@ -122,9 +122,9 @@ def mahal(x):
 **기본값**: 매 EDA에 PCA + correlation heatmap, 매 supervised에 LDA, 매 latent factor에 FA + varimax.

 ## 🔗 Graph
- 부모: [[Statistics]] · [[Linear-Algebra]]
- 변형: [[PCA]] · [[Factor-Analysis]] · [[CCA]] · [[LDA]] · [[MANOVA]]
- 응용: [[EDA]] · [[Feature-Engineering]] · [[Biostatistics]]
+- 부모: [[Statistics]] · [[Linear-Algebra-Foundations|Linear-Algebra]]
+- 변형: [[PCA]] · [[Factor-Analysis]] · [[LDA]]
+- 응용: [[EDA]] · [[Feature Engineering|Feature-Engineering]]
 - Adjacent: [[Dimensionality-Reduction]] · [[t-SNE]] · [[UMAP]]

 ## 🤖 LLM 활용
@@ -108,9 +108,7 @@ print(mine.mic())
 **기본값**: KSG for analysis, InfoNCE for SSL training.

 ## 🔗 Graph
- 부모: [[Information Theory]] · [[Information-Entropy]]
- 변형: [[Conditional-Mutual-Information]] · [[Pointwise-MI]] · [[Total-Correlation]]
- 응용: [[Self-Supervised-Learning]] · [[Feature-Selection]] · [[Information-Bottleneck]]
+- 부모: [[Entropy in Information Theory|Information Theory]] · [[Information-Entropy]]
 - Adjacent: [[Kullback-Leibler-Divergence]] · [[Cross-Entropy]] · [[Causal-Inference]]

 ## 🤖 LLM 활용
@@ -118,10 +118,10 @@ def mece_pareto(buckets, values, top=0.8):
 **기본값**: Quantitative MECE (numerical sum-check 가능).

 ## 🔗 Graph
- 부모: [[Mental Models]] · [[Pyramid-Principle]]
+- 부모: [[Mental_Models|Mental Models]] · [[Pyramid-Principle]]
 - 변형: [[Logic Trees]] · [[Issue Tree]]
- 응용: [[Consulting-Frameworks]] · [[LLM-Agent-Planning]] · [[Root-Cause-Analysis]]
- Adjacent: [[Decision Theory]] · [[Set-Theory]]
+- 응용: [[Root-Cause-Analysis]]
+- Adjacent: [[Decision Theory]]

 ## 🤖 LLM 활용
 **언제**: Task decomposition, structured analysis, taxonomy design, agent planning.
@@ -104,9 +104,8 @@ def pac(low_phase, high_amp):

 ## 🔗 Graph
 - 부모: [[Global-Workspace-Theory]] · [[Cognitive Neuroscience of Flow]]
- 변형: [[Conscious-Access]] · [[P300]] · [[Late-Positive-Potential]]
- 응용: [[Anesthesia-Monitoring]] · [[Disorders-of-Consciousness]] · [[AI-Consciousness-Benchmarks]]
- Adjacent: [[Cross-Frequency Coupling (CFC)]] · [[Attention-Networks]] · [[Integrated-Information-Theory]]
+- 변형: [[Conscious-Access]]
+- Adjacent: [[Cross-Frequency Coupling (CFC)]]

 ## 🤖 LLM 활용
 **언제**: Consciousness modeling, EEG ignition analysis, GWT-inspired AI architecture.
@@ -98,10 +98,8 @@ def forward_diffuse(x0, t, betas):
 **기본값**: Additive Gaussian (most analyzable).

 ## 🔗 Graph
- 부모: [[Information Theory]] · [[Statistics]]
- 변형: [[White-Noise]] · [[Pink-Noise]] · [[Shot-Noise]]
- 응용: [[Denoising-Diffusion]] · [[Kalman-Filter-and-State-Tracking]] · [[Differential-Privacy]]
- Adjacent: [[Signal-Processing]] · [[Stochastic-Process]]
+- 부모: [[Entropy in Information Theory|Information Theory]] · [[Statistics]]
+- 응용: [[Kalman-Filter-and-State-Tracking]] · [[Differential-Privacy]]

 ## 🤖 LLM 활용
 **언제**: Data augmentation, robustness training, generative modeling, privacy.
@@ -167,9 +167,8 @@ def align_classes(onto_a_labels, onto_b_labels, threshold=0.85):

 ## 🔗 Graph
 - 부모: [[Knowledge Graph]] · [[Semantic Web]] · [[Knowledge Representation]]
- 변형: [[OWL]] · [[RDFS]] · [[SKOS]]
- 응용: [[Gene Ontology]] · [[schema.org]] · [[Wikidata]] · [[GraphRAG]]
- Adjacent: [[Description Logic]] · [[SPARQL]] · [[Linked Data]] · [[Taxonomy]]
+- 변형: [[OWL]]
+- 응용: [[GraphRAG]]

 ## 🤖 LLM 활용
 **언제**: 매 hallucination 감소를 위한 grounding, 매 enterprise data fabric, 매 named-entity resolution against canonical IDs.
@@ -132,8 +132,8 @@ def mmc(lam, mu, c):

 ## 🔗 Graph
 - 부모: [[Optimization]] · [[Mathematical-Programming]]
- 변형: [[Linear-Programming]] · [[Integer-Programming]] · [[Stochastic-Programming]]
- 응용: [[Supply-Chain]] · [[Vehicle-Routing-Problem]] · [[Revenue-Management]]
+- 변형: [[Linear-Programming]] · [[Integer-Programming]]
+- 응용: [[Supply-Chain]]
 - Adjacent: [[Reinforcement-Learning]] · [[Combinatorial-Optimization]]

 ## 🤖 LLM 활용
@@ -102,10 +102,8 @@ def rkhs_eval(alpha, X, x_new, kernel):
 **기본값**: Self-adjoint compact (가장 잘 분석됨).

 ## 🔗 Graph
- 부모: [[Linear-Algebra-Foundations]] · [[Functional-Analysis]]
- 변형: [[Self-Adjoint-Operator]] · [[Compact-Operator]] · [[Unitary-Operator]]
- 응용: [[Quantum-Mechanics]] · [[Koopman-Operator]] · [[RKHS]]
- Adjacent: [[Spectral-Theorem]] · [[Hilbert-Space]] · [[Eigenvalues-and-Eigenvectors]]
+- 부모: [[Linear-Algebra-Foundations]]
+- Adjacent: [[Hilbert-Space]] · [[Eigenvalues-and-Eigenvectors]]

 ## 🤖 LLM 활용
 **언제**: Infinite-dim linear systems, PDE/quantum modeling, operator-learning (FNO, DeepONet).
@@ -151,9 +151,8 @@ def value_iter(grid, f, l, dt, gamma=0.99, n_iter=500):

 ## 🔗 Graph
 - 부모: [[Control-Theory]] · [[Optimization]]
- 변형: [[LQR]] · [[Model-Predictive-Control]] · [[iLQR]] · [[Differential-Dynamic-Programming]]
- 응용: [[Robotics]] · [[Autonomous-Driving]] · [[Aerospace-GNC]]
- Adjacent: [[Reinforcement-Learning]] · [[Dynamic-Programming]] · [[Bellman-Equation]]
+- 응용: [[Robotics]] · [[Autonomous-Driving]]
+- Adjacent: [[Reinforcement-Learning]] · [[Dynamic-Programming]] · [[데이터_사이언스_및_ML_엔지니어링|Bellman-Equation]]

 ## 🤖 LLM 활용
 **언제**: cost-function design, MPC weight tuning rationale, Pontryagin/HJB derivation 매 explanation.
@@ -167,10 +167,10 @@ res = minimize(f, x0, jac=grad, hess=hess, method="trust-ncg")
 **기본값**: 매 ML training → AdamW; 매 cost-expensive HP → BO; 매 industrial LP/MILP → Gurobi.

 ## 🔗 Graph
- 부모: [[Mathematics]] · [[Numerical Analysis]] · [[Operations-Research]]
- 변형: [[Convex Optimization]] · [[Stochastic Optimization]] · [[Combinatorial Optimization]]
- 응용: [[Deep Learning]] · [[Reinforcement Learning]] · [[Hyperparameter Tuning]] · [[Logistics]]
- Adjacent: [[Gradient Descent]] · [[Adam]] · [[L-BFGS]] · [[Bayesian Optimization]] · [[CMA-ES]]
+- 부모: [[Operations-Research]]
+- 변형: [[Combinatorial Optimization]]
+- 응용: [[Deep Learning]] · [[Reinforcement Learning]]
+- Adjacent: [[Gradient Descent]] · [[Adam]] · [[Bayesian Optimization]] · [[CMA-ES]]

 ## 🤖 LLM 활용
 **언제**: 매 PEFT (LoRA) AdamW config, 매 RLHF PPO/DPO step, 매 hyperparameter search via Optuna.
@@ -128,10 +128,8 @@ def proj_simplex(v):
 **기본값**: ML training은 AdamW + cosine; convex은 CVXPY; black-box는 Optuna.

 ## 🔗 Graph
- 부모: [[Mathematics]] · [[Calculus]]
- 변형: [[Convex-Optimization]] · [[Nonconvex-Optimization]] · [[Stochastic-Optimization]]
- 응용: [[Deep-Learning-Training]] · [[Operations-Research]] · [[Optimal-Control-Theory]]
- Adjacent: [[Linear-Algebra]] · [[Numerical-Methods]]
+- 응용: [[Operations-Research]] · [[Optimal-Control-Theory]]
+- Adjacent: [[Linear-Algebra-Foundations|Linear-Algebra]]

 ## 🤖 LLM 활용
 **언제**: optimizer recipe selection, hyperparam search prior, KKT/Lagrangian derivation 매 explanation.
@@ -157,10 +157,10 @@ X_proj = rp.fit_transform(X)  # 매 Johnson-Lindenstrauss 보존
 **기본값**: 매 baseline PCA → 매 visualization UMAP → 매 representation learning contrastive.

 ## 🔗 Graph
- 부모: [[Linear-Algebra-Foundations]] · [[Statistics]] · [[Unsupervised Learning]]
- 변형: [[Kernel PCA]] · [[Sparse PCA]] · [[Probabilistic PCA]] · [[ICA]]
- 응용: [[Visualization]] · [[scRNA-seq]] · [[Embedding Analysis]] · [[Feature Engineering]]
- Adjacent: [[t-SNE]] · [[UMAP]] · [[Autoencoder]] · [[NMF]] · [[Random Projection]]
+- 부모: [[Linear-Algebra-Foundations]] · [[Statistics]]
+- 변형: [[ICA]]
+- 응용: [[Feature Engineering]]
+- Adjacent: [[t-SNE]] · [[UMAP]] · [[Autoencoder]]

 ## 🤖 LLM 활용
 **언제**: 매 embedding analysis, 매 anisotropy whitening, 매 visualizing high-dim attention/activations.
@@ -151,8 +151,7 @@ def batch_size_pid(target_tps, current_tps, pid_state):

 ## 🔗 Graph
 - 부모: [[Control-Theory]] · [[Feedback-Control-Systems]]
- 변형: [[Cascade Control]] · [[Gain Scheduling]] · [[Adaptive PID]]
- 응용: [[Robotics]] · [[Drone Stabilization]] · [[LLM Serving]] · [[RLHF]]
+- 응용: [[Robotics]] · [[RLHF]]
 - Adjacent: [[Model-Predictive-Control (MPC)]] · [[Kalman-Filter-and-State-Tracking]] · [[Reinforcement-Learning]]

 ## 🤖 LLM 활용
@@ -160,10 +160,7 @@ class SpectralConv1d(nn.Module):
 **기본값**: classical solving은 FEM (FEniCSx) or FVM (OpenFOAM); ML-side는 FNO; inverse problem은 PINN.

 ## 🔗 Graph
- 부모: [[Mathematics]] · [[Numerical-Methods]] · [[Calculus]]
- 변형: [[Heat-Equation]] · [[Wave-Equation]] · [[Navier-Stokes]] · [[Schrodinger-Equation]]
- 응용: [[CFD]] · [[Structural-Mechanics]] · [[Option-Pricing]]
- Adjacent: [[PINN]] · [[Neural-Operator]] · [[Diffusion-Models]] · [[Finite-Element-Method]]
+- Adjacent: [[PINN]] · [[Diffusion-Models]] · [[Finite-Element-Method]]

 ## 🤖 LLM 활용
 **언제**: PDE classification 설명, BC formulation help, weak form derivation, PINN architecture suggestion.
@@ -143,10 +143,10 @@ def soft_resample(w, alpha=0.5):
 **기본값**: Bootstrap PF with systematic resampling (ESS<N/2 trigger), N=1000-10000.

 ## 🔗 Graph
- 부모: [[Bayesian-Filtering]] · [[Sequential-Monte-Carlo]]
- 변형: [[Bootstrap-Filter]] · [[Auxiliary-Particle-Filter]] · [[Rao-Blackwellized-PF]]
- 응용: [[SLAM]] · [[Target-Tracking]] · [[Stochastic-Volatility]]
- Adjacent: [[Kalman-Filter]] · [[Extended-Kalman-Filter]] · [[Unscented-Kalman-Filter]]
+- 부모: [[Sequential-Monte-Carlo]]
+- 변형: [[Bootstrap-Filter]]
+- 응용: [[SLAM]]
+- Adjacent: [[Kalman-Filter-and-State-Tracking|Kalman-Filter]]

 ## 🤖 LLM 활용
 **언제**: PF tuning rationale (N choice, resample threshold), proposal design discussion, debug degenerate weights.
@@ -132,9 +132,8 @@ estimator.plot()

 ## 🔗 Graph
 - 부모: [[Cross-Frequency Coupling (CFC)]] · [[Signal-Processing-Foundations]]
- 변형: [[Theta-Gamma Coupling]] · [[Phase-Phase-Coupling]]
- 응용: [[EEG-Analysis]] · [[Sleep-Staging]] · [[Brain-Computer-Interfaces]]
- Adjacent: [[Hilbert-Transform]] · [[Wavelet-Transform]] · [[Neural Ignition]]
+- 변형: [[Theta-Gamma Coupling]]
+- Adjacent: [[Neural Ignition]]

 ## 🤖 LLM 활용
 **언제**: PAC method 선택 explain, comodulogram 결과 interpretation.
--- a/Show More
+++ b/Show More