The Metropolis–Hastings (MH) algorithm is a Markov Chain Monte Carlo (MCMC) technique widely used to sample from complex probability distributions. Specifically, this report employs a Random Walk Metropolis–Hastings approach, in which proposed updates to the parameter values are generated by random perturbations around the current state. This algorithm enables efficient approximation the target distribution by drawing a series of dependent samples.

More robust and deep version from 2024. Include Hyperparameter tuning and cross-validation as well as detailed explanation

This report delineates the application of supervised and unsupervised machine learning methodologies to analyze datasets for predictive and exploratory purposes, respectively.