Efficient Online Smoothing in General Hidden Markov Models
This thesis discusses the problem of estimating smoothed expectations of sums of additive functionals of sequences of hidden states in general hidden Markov models. To compute expectations of this sort, the smoothing distribution, i.e. the conditional distribution of the hidden states given the corresponding observations, needs to be approximated. This thesis proposes a new algorithm to achieve th