Forward–backward algorithm: Difference between revisions

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The '''forward–backward algorithm''' is an [[inference]] [[algorithm]] for [[hidden Markov models]] which computes the [[posterior probability|posterior]] [[marginal probability|marginals]] of all hidden state variables given a sequence of observations/emissions <math>o_{1:t}:= o_1,\dots,o_t</math>, i.e. it computes, for all hidden state variables <math>X_k \in \{X_1, \dots, X_t\}</math>, the distribution <math>P(X_k\ |\ o_{1:t})</math>. This inference task is usually called ''smoothing''. The algorithm makes use of the principle of [[dynamic programming]] to efficiently compute efficiently the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in time; hence the name ''forward–backward algorithm''.

The term ''forward–backward algorithm'' is also used to refer to any algorithm belonging to the general class of algorithms that operate on sequence models in a forward–backward manner. In this sense, the descriptions in the remainder of this article refer but to one specific instance of this class.