In computer science and statistical computing , the Baum-Welch algorithm is used to find the unknown parameters of a hidden Markov model (HMM). It is also known as the forward-backward algorithm.

The Baum-Welch algorithm is an EM (expectation-maximization) algorithm. It can compute maximum likelihood estimates and posterior mode estimates for the parameters (transition and emission probabilities) of an HMM, when given only emissions as training data.

The algorithm has two steps: (1) calculating the forward probability and the backward probability for each HMM state; (2) on the basis of this, determining the frequency of the transition-emission pair values and dividing it by the probability of the entire string. This amounts to calculating the expected count of the particular transition-emission pair. Each time a particular transition is found, the value of the quotient of the transition divided by the probability of the entire string goes up, and this value can then be made the new value of the transition.

See also

• forward algorithm