This algorithm improves decoding efficiency in hidden markov models, highlighting advances over viterbi and PMAP methods.
Hidden Markov models (HMMs) are characterized by an unobservable Markov chain and an observable process—a noisy version of the hidden chain. Decoding the original signal from the noisy observations is one of the main goals in nearly all HMM based data analyses. Existing decoding algorithms such as Viterbi and the pointwise maximum a posteriori (PMAP) algorithm have computational complexity at best linear in the length of the observed sequence, and sub-quadratic in the size of the state space of the hidden chain.
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Mösching et al. (2025) studied this question.
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