Markov models

Metropolis–Hastings algorithm

In statistics and statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution from which direct sampling is difficult.

The algorithm was named after Nicholas Metropolis, who authored the 1953 paper Equation of State Calculations by Fast Computing Machines together with Arianna W. Rosenbluth, Marshall Rosenbluth, Augusta H. Teller and Edward Teller.

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Forward–backward algorithm

The forward-backward algorithm is an algorithm for computing posterior marginals in a hidden Markov model (HMM). It is based on dynamic programming, and has linear complexity in the length of the sequence. It is used as a component of several other algorithms, such as the Baum_Welch algorithm and block Gibbs sampling in factorial HMMs. <read on>


The World Wide Web would be a World Wide Jungle if it weren’t for this algorithm. Named after Google’s co-founder Larry Page, the algorithm organizes all the worlds information (well, mostly webpages) and makes it accessible. <read on>