Non-Reversible Markov Chains for Bayesian Mixture Models

Provides Bayesian inference for finite and infinite mixture models using Markov chain Monte Carlo (MCMC) methods. Supports Gaussian, multivariate Gaussian, Poisson, Binomial, and multivariate Bernoulli mixture kernels with different covariance structures and flexible prior specifications. Provides tools for data generation, posterior density evaluation, clustering and similarity analysis, and advanced MCMC sampling strategies, including reversible and non-reversible samplers: Ascolani et al. (2026) , informed proposals: Zanella (2020) , collapsed Gibbs sampling: Diebolt and Robert (1994) , and split-and-merge samplers: Dahl and Newcomb (2021) .