Simulation Study of the Metropolis-Hastings Algorithm for Distribution Sampling
A simulation-based study of the Metropolis-Hastings algorithm using Python. This project explores MCMC sampling from t-distributions and normal distributions with both unconditional and conditional proposals. Includes visual analysis using histograms and QQ plots. Developed as part of a graduate-level course in statistical inference. Highly relevant for Bayesian inference, probabilistic modeling, and ML research.
May 1, 2024
