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arXiv · 0704.0062

On-line Viterbi Algorithm and Its Relationship to Random Walks

Rastislav Šrámek·Broňa Brejová·Tomáš Vinař
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Abstract

In this paper, we introduce the on-line Viterbi algorithm for decoding hidden Markov models (HMMs) in much smaller than linear space. Our analysis on two-state HMMs suggests that the expected maximum memory used to decode sequence of length $n$ with $m$-state HMM can be as low as $Θ(m\log n)$, without a significant slow-down compared to the classical Viterbi algorithm. Classical Viterbi algorithm requires $O(mn)$ space, which is impractical for analysis of long DNA sequences (such as complete human genome chromosomes) and for continuous data streams. We also experimentally demonstrate the performance of the on-line Viterbi algorithm on a simple HMM for gene finding on both simulated and real DNA sequences.

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Rastislav Šrámek, Broňa Brejová, Tomáš Vinař. 2007-03-31. On-line Viterbi Algorithm and Its Relationship to Random Walks. https://doi.org/10.1007/978-3-540-74126-8_23

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