Gibbs Algorithm Implementation in MATLAB for Pattern Classification

This article presents a MATLAB implementation of the Gibbs sampling algorithm, specifically designed for pattern classification applications. The Gibbs algorithm is a Markov Chain Monte Carlo (MCMC) sampling method widely utilized across various disciplines including statistics, physics, and computer science. In this implementation, the algorithm approximates probability distributions through multiple iterative sampling cycles. The MATLAB code structure typically includes key functions for conditional probability calculations and state transitions, generating a sequence of samples that preserve specific characteristics of the original dataset. These generated samples can subsequently be employed for pattern recognition and classification tasks. The implementation demonstrates how Gibbs sampling serves as a powerful computational tool for solving practical problems by efficiently exploring high-dimensional probability spaces through coordinate-wise sampling from conditional distributions.