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Quantitative Methods in DemographySpreading Disease Modeling Using Markov Random Fields

Quantitative Methods in Demography: Spreading Disease Modeling Using Markov Random Fields [Markov random fields are widely used to model spatial processes. Key components of any statistical analysis using such models are the choice of an appropriate model as the prior distribution and the estimation of prior model parameters. Models for spreading diseases are given based on whether or not the disease succeeds or fails to appear in the region. In this work, the spatial pattern models for spreading diseases have been analyzed considering Markov random fields auto-models. The Gibbs sampler would be used to simulate example images for various parameter combinations.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Quantitative Methods in DemographySpreading Disease Modeling Using Markov Random Fields

Part of the The Springer Series on Demographic Methods and Population Analysis Book Series (volume 52)
Editors: Skiadas, Christos H.; Skiadas, Charilaos
Zimeras, Stelios
Quantitative Methods in Demography — May 31, 2022
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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-030-93004-2
Pages
155 –163
DOI
10.1007/978-3-030-93005-9_10
Publisher site
See Chapter on Publisher Site

Abstract

[Markov random fields are widely used to model spatial processes. Key components of any statistical analysis using such models are the choice of an appropriate model as the prior distribution and the estimation of prior model parameters. Models for spreading diseases are given based on whether or not the disease succeeds or fails to appear in the region. In this work, the spatial pattern models for spreading diseases have been analyzed considering Markov random fields auto-models. The Gibbs sampler would be used to simulate example images for various parameter combinations.]

Published: May 31, 2022

Keywords: Spreading disease; Markov random fields; Bayesian analysis; Estimation techniques; Auto-logistic; Auto-binomial models

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