METROLOGICAL BENCHMARK: BIDSIM VS. GIBBS SAMPLING

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).