TR2001-022 – Understanding Belief Propagation and its Generalizations. Understanding Belief Propagation and its Generalizations The belief equation b 1245 = k[φ 1 φ 2 φ 4 φ 5 ψ 12 ψ 14 ψ 25 ψ 45 ][m 36→25 m 78→45 m 6→5 m 8→5 ], for the region [1245], illustrated both on the region graph (left) and on the original pairwise MRF (right).
"Inference" problems arise in statistical physics, computer vision, error-correcting coding theory, and AI. We explain the principles behind the belief propagation (BP) algorithm, which is an efficient way to solve inference problems based on passing local messages. We develop a unified approach with examples, notation, and graphical models borrowed from the relevant disciplines.We explain the close connection between the BP algorithm and the Bethe approximation of statistical physics. In particular, we show that BP can only converge to a fixed point that is also a stationary point of the Bethe approximation to the free energy. Lecture - 21 Reasoning Under Uncertainity. Bayes' Theorem. An Intuitive Explanation of Bayes' Theorem Bayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction.
Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule", or something called Bayesian reasoning. They sound really enthusiastic about it, too, so you google and find a webpage about Bayes' Theorem and... It's this equation. That's all.