ABSTRACT
The graphs used in graphical models are the exact ones that are taught in basic algorithms classes: a set of nodes, together with links between them, which can be either directed or not. The basic concept of the graphical model is very simple, which makes it all the more amazing that it produces a powerful set of tools for understanding and creating machine learning algorithms. The basic idea of using Markov Chain Monte Carlo methods in Bayesian networks is to sample from the hidden variables, and then weight the samples by their likelihoods. The Hidden Markov Model is one of the most popular graphical models. It is used in speech processing and in a lot of statistical work. The Hidden Markov Model generally works on a set of temporal data. As well as the linearity of the function, the Kalman filter also assumes that the distributions are Gaussian, so that they can be convolved and stay as Gaussians.
