Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. The forward-backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations/emissions o_{1:t}:= o_1,dots,o_t, i.e. it computes, for all hidden state variables X_k in {X_1, do ...Täydellinen kuvaus
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. The forward-backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations/emissions o_{1:t}:= o_1,dots,o_t, i.e. it computes, for all hidden state variables X_k in {X_1, dots, X_t}, the distribution P(X_k | o_{1:t}). This inference task is usually called smoothing. The algorithm makes use of the principle of dynamic programming to efficiently compute 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.