Hidden Markov model with missing emissions

Karima Elkimakh () and Abdelaziz Nasroallah
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Karima Elkimakh: LAMIGEP laboratory-Moroccan School of Engineering Sciences (EMSI)
Abdelaziz Nasroallah: Libma Laboratory-Faculty of Sciences Semlalia, Cadi Ayyad University

Computational Statistics, 2024, vol. 39, issue 2, No 1, 385-403

Abstract: Abstract In a Hidden Markov model (HMM), from hidden states, the model generates emissions that are visible. Generally, the problems to be solved by such models, are based on such emissions that are considered as observed data. In this work, we propose to study the case where some emissions are missing in a given emission sequence using different techniques, in particular a split technique which reduces the computational cost. Mainly we resolve the fundamental problems of an HMM with a lack of observations. The algorithms obtained following this approach are successfully tested through numerical examples.

Keywords: Hidden Markov model; Markov chain; Forward and backward probabilities; Viterbi algorithm; Baum–Welch algorithm; Monte Carlo simulation; Missing observations; Qualitative data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-022-01285-6

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