Bayesian Tool Based on Markov Chain Monte Carlo Theory

This program is a Bayesian analysis tool developed based on Markov Chain Monte Carlo theory, capable of effectively handling machine learning and Gaussian process problems. The implementation uses MCMC Methods for MLP (Multi-Layer Perceptron) and GP (Gaussian Process) and Stuff for MATLAB Version 2.1, which demonstrates improved computational efficiency and enhanced robustness compared to previous versions through optimized sampling algorithms and better convergence handling. Users can customize various parameters including chain length, burn-in period, and proposal distributions to achieve optimal results for their specific applications. The package includes comprehensive documentation detailing the implementation of Metropolis-Hastings and Gibbs sampling algorithms, along with practical examples demonstrating key functions for posterior distribution estimation and model comparison. The tool provides reliable solutions for complex statistical modeling problems through its well-tested MCMC framework and user-friendly interface design.