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Nested Sampling with Slice-within-Gibbs: Efficient Evidence Calculation for Hierarchical Bayesian Models

1. 연구 배경 및 동기 Nested Sampling 은 베이지안 증거를 직접 추정함으로써 모델 비교에 강점을 가지지만, 고차원에서 제한된 사전 샘플링 이 병목이 된다. 현재 대부분의 구현은 블랙박스 MCMC (ellipsoidal, slice 등) 를 사용해 모델 구조를 활용하지 못한다. Gradient‑기반 샘플러(HMC, NUTS) 가 고차원에서 효율적이지만, 가능도 제약 을 만족시키는 변형은 아직 미성숙하다. 따라서 가능도 구조가 블록‑분해 가능한 계층 모델 에 특화된 샘플링 기법이 절실히 필요했다. 2. 핵심 기법 | 요소

Nested Sampling with Slice-within-Gibbs: Efficient Evidence Calculation for Hierarchical Bayesian Models

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