forward-backward algorithm

(1.8 hours to learn)


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.


This concept has the prerequisites:

Core resources (read/watch one of the following)



Supplemental resources (the following are optional, but you may find them useful)


Bayesian Reasoning and Machine Learning
A textbook for a graudate machine learning course.
Author: David Barber


See also