Metrological Benchmark: Bidsim Vs. Gibbs Sampling
Year: 2026
Type: article
Abstract: In high-dimensional categorical Bayesian inference, researchers frequently rely on Markov Chain Monte Carlo (MCMC) methods such as Gibbs Sampling, Metropolis-Hastings, or Hamiltonian Monte Carlo (HMC/NUTS). While Gibbs sampling decomposes a joint posterior probability distribution by sampling each variable sequentially from its conditional distribution, it inherently remains a stochastic Markov chain algorithm. Consequently, Gibbs sampling suffers from severe sample autocorrelation, burn-in CPU waste, and runtime non-determinism. In contrast, BIDSim (Chained Virtual Urns Simulator) replaces Markov chain iterations entirely with a two-phase hybrid architecture: deterministic knowledge compilation followed by ultra-fast, independent scenario draws with zero autocorrelation (lag-1 = 0.000) and 100% Effective Sample Size (ESS).
Author: Pargamin, Bernard
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225739895
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