Editing Hidden Markov model (section)

==== Smoothing ====
This is similar to filtering but asks about the distribution of a latent variable somewhere in the middle of a sequence, i.e. to compute <math>P(x(k)\mid y(1), \dots, y(t))</math> for some <math>k < t</math>. From the perspective described above, this can be thought of as the probability distribution over hidden states for a point in time ''k'' in the past, relative to time ''t''.

The [[forward-backward algorithm]] is a good method for computing the smoothed values for all hidden state variables.