Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
On the memory complexity of the forward-backward algorithm
Pattern Recognition Letters
Compact encoding of stationary Markov sources
IEEE Transactions on Information Theory
Decision making in Markov chains applied to the problem of pattern recognition
IEEE Transactions on Information Theory
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We illustrate the Efficient Forward Filtering Backward Smoothing (EFFBS) algorithm proposed by Khreich et al. (2010) for estimation of hidden Markov models. The algorithm is aimed at reducing the amount of memory required by the Baum-Welch recursions, while having the same complexity in terms of number of operations. In implementing the EFFBS algorithm we found a numerical problem that limits its applicability. We discuss this problem in detail, providing some possible explanations of the causes of the error, together with two illustrative examples.