Temporal Parallelization of Inference in Hidden Markov Models

This paper presents algorithms for the parallelization of inference in hidden Markov models (HMMs). In particular, we propose a parallel forward-backward type of filtering and smoothing algorithm as well as a parallel Viterbi-type maximum-a-posteriori (MAP) algorithm. We define associative elements...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 69; pp. 4875 - 4887
Main Authors: Hassan, Sakira, Sarkka, Simo, Garcia-Fernandez, Angel
Format: Journal Article
Language:English
Published: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Graphics processing units
Hidden Markov models
Inference
Inference algorithms
Markov chains
Parallel forward-backward algorithm
parallel max-product algorithm
Parallel processing
parallel sum-product algorithm
parallel Viterbi algorithm
Polytopes
Signal processing algorithms
Sum product algorithm
Task analysis
Viterbi algorithm
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:This paper presents algorithms for the parallelization of inference in hidden Markov models (HMMs). In particular, we propose a parallel forward-backward type of filtering and smoothing algorithm as well as a parallel Viterbi-type maximum-a-posteriori (MAP) algorithm. We define associative elements and operators to pose these inference problems as all-prefix-sums computations and parallelize them using the parallel-scan algorithm. The advantage of the proposed algorithms is that they are computationally efficient in HMM inference problems with long time horizons. We empirically compare the performance of the proposed methods to classical methods on a highly parallel graphics processing unit (GPU).
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2021.3103338