Demystifying the Gibbs Algorithms in Machine Learning

Introduction

Also known as Gibbs Sampling it is a powerful class of Markov Chain Monte Carlo (MCMC) methods used for sampling in Machine Learning

Basics 

Technique that iteratively samples from conditional probability distributions, enabling efficient inference in complex probabilistic models

 Advantages

Includes simplicity, applicability to complex models, and ability to handle missing data & large-scale problems

Applications

Immunology Image processing (Lattice model) Bioinformatics (analyzing DNA strands) Segregation & survival analysis

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