This repository includes some homemade lectures for the people who are approaching the study of Conditional Random Fields and Markov Random Fields, so already having some preliminary background ( Probability, Statistics, MCMC, Gibbs Sampling, Maximum Likelihood Estimantion, Maximum A Posteriori, and so on) but have some missing points to put all together.
What I'd like to do here, is to take some articles and explain them step by step, without giving anything for granted. I would like to explain all the reasoning process of how the problems have been approached.
- Image Denoising with Gibb Sampling Clearly Explained