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Existence of infinite Viterbi path for pairwise Markov models

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  • Lember, Jüri
  • Sova, Joonas

Abstract

For hidden Markov models one of the most popular estimates of the hidden chain is the Viterbi path — the path maximizing the posterior probability. We consider a more general setting, called the pairwise Markov model, where the joint process consisting of finite-state hidden regime and observation process is assumed to be a Markov chain. We prove that under some conditions it is possible to extend the Viterbi path to infinity for almost every observation sequence which in turn enables to define an infinite Viterbi decoding of the observation process, called the Viterbi process. This is done by constructing a block of observations, called a barrier, which ensures that the Viterbi path goes through a given state whenever this block occurs in the observation sequence.

Suggested Citation

  • Lember, Jüri & Sova, Joonas, 2020. "Existence of infinite Viterbi path for pairwise Markov models," Stochastic Processes and their Applications, Elsevier, vol. 130(3), pages 1388-1425.
  • Handle: RePEc:eee:spapps:v:130:y:2020:i:3:p:1388-1425
    DOI: 10.1016/j.spa.2019.05.004
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    1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    2. Lember, Jüri, 2011. "On approximation of smoothing probabilities for hidden Markov models," Statistics & Probability Letters, Elsevier, vol. 81(2), pages 310-316, February.
    3. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    4. Lember, Jüri & Matzinger, Heinrich & Sova, Joonas & Zucca, Fabio, 2018. "Lower bounds for moments of global scores of pairwise Markov chains," Stochastic Processes and their Applications, Elsevier, vol. 128(5), pages 1678-1710.
    5. Kristi Kuljus & Jüri Lember, 2016. "On the Accuracy of the MAP Inference in HMMs," Methodology and Computing in Applied Probability, Springer, vol. 18(3), pages 597-627, September.
    6. Lember, Jüri, 2011. "A correction on approximation of smoothing probabilities for hidden Markov models," Statistics & Probability Letters, Elsevier, vol. 81(9), pages 1463-1464, September.
    7. Derrode, Stéphane & Pieczynski, Wojciech, 2013. "Unsupervised data classification using pairwise Markov chains with automatic copulas selection," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 81-98.
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    Cited by:

    1. Kristi Kuljus & Jüri Lember, 2023. "Pairwise Markov Models and Hybrid Segmentation Approach," Methodology and Computing in Applied Probability, Springer, vol. 25(2), pages 1-32, June.
    2. Jüri Lember & Joonas Sova, 2021. "Regenerativity of Viterbi Process for Pairwise Markov Models," Journal of Theoretical Probability, Springer, vol. 34(1), pages 1-33, March.

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