A simplified matrix formulation for sensitivity analysis of hidden Markov models
type: Item
instance of: scholarly article
title: A simplified matrix formulation for sensitivity analysis of hidden Markov models
zbMATH Open document ID: 1461.62148
author: Seifemichael B. Amsalu
author: A. Homaifar
author: Albert C. Esterline
published in: Algorithms
publication date: 2018-08-13T00:00:00Z
review text: Summary: In this paper, a new algorithm for sensitivity analysis of discrete hidden Markov models (HMMs) is proposed. Sensitivity analysis is a general technique for investigating the robustness of the output of a system model. Sensitivity analysis of probabilistic networks has recently been studied extensively. This has resulted in the development of mathematical relations between a parameter and an output probability of interest and also methods for establishing the effects of parameter variations on decisions. Sensitivity analysis in HMMs has usually been performed by taking small perturbations in parameter values and re-computing the output probability of interest. As recent studies show, the sensitivity analysis of an HMM can be performed using a functional relationship that describes how an output probability varies as the network's parameters of interest change. To derive this sensitivity function, existing Bayesian network algorithms have been employed for HMMs. These algorithms are computationally inefficient as the length of the observation sequence and the number of parameters increases. In this study, a simplified efficient matrix-based algorithm for computing the coefficients of the sensitivity function for all hidden states and all time steps is proposed and an example is presented.
Mathematics Subject Classification ID: 62M05
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62J10
zbMATH DE Number: 6916756
zbMATH Keywords: sensitivity analysis
zbMATH Keywords: hidden Markov models
zbMATH Keywords: Bayesian network
describes a project that uses: SamIam
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a10030097
OpenAlex ID: W2749578578
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DOI: 10.3390/A10030097
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description: scientific article; zbMATH DE number 6916756