Bayesian Inference Toolkit Based on Markov Chain Monte Carlo (MCMC) Sampling

This source code provides a Bayesian inference toolkit based on Markov Chain Monte Carlo (MCMC) methods. MCMC sampling serves as an effective approach for Bayesian analysis, enabling more accurate data modeling and prediction capabilities. The toolkit implements core MCMC sampling algorithms alongside Gaussian classification methods built upon MCMC framework, suitable for data classification and pattern recognition tasks. The package includes detailed implementations of key sampling techniques such as Metropolis-Hastings algorithm and Gibbs sampling, providing flexibility for diverse data analysis requirements. The implementation features modular code structure with separate functions for proposal distribution handling, acceptance probability calculation, and chain convergence monitoring. Comprehensive documentation accompanies the toolkit, featuring usage examples and parameter configuration guidelines to facilitate rapid adoption. By leveraging this toolkit, users can efficiently perform Bayesian analysis and data modeling, delivering more precise and reliable results for statistical inference applications.