Parameter and Mixture Component Estimation in Spatial Hidden Markov Models: A Comparative Analysis of Computational Methods

Eugene A. Opoku, Syed Ejaz Ahmed, Trisalyn Nelson, Farouk S. Nathoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hidden Markov models incorporating the Potts model for the labelling process are an important class of mixture models in spatial statistics. These models have been applied to problems in statistical mechanics, image analysis and disease mapping, among other areas. Jointly estimating the model parameters, the discrete state variables and the number of states (number of mixture components) is recognized as a difficult combinatorial optimization problem. We make comparisons between iterated conditional modes (ICM), simulated annealing (SA) and hybrid ICM with ant colony system (ACS-ICM) optimization for pixel labelling, parameter estimation and mixture component estimation. These comparisons are made for different levels of spatial dependence in the underlying true image. Our studies demonstrate that estimation based on ACS-ICM when carefully tuned exhibits performance that is uniformly superior to both ICM as well as a carefully tuned SA algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the 14th International Conference on Management Science and Engineering Management, ICMSEM 2020 - Volume 1
EditorsJiuping Xu, Gheorghe Duca, Syed Ejaz Ahmed, Fausto Pedro García Márquez, Asaf Hajiyev
PublisherSpringer
Pages340-355
Number of pages16
ISBN (Print)9783030498283
DOIs
StatePublished - 2020
Externally publishedYes
Event14th International Conference on Management Science and Engineering Management, ICMSEM 2020 - Chisinau, Moldova, Republic of
Duration: Jul 30 2020Aug 2 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1190 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference14th International Conference on Management Science and Engineering Management, ICMSEM 2020
CountryMoldova, Republic of
CityChisinau
Period7/30/208/2/20

Keywords

  • Ant colony system optimization
  • Hidden Markov random field
  • Iterated conditional modes
  • Mixture model
  • Potts model
  • Pseudo-likelihood
  • Simulated annealing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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  • Cite this

    Opoku, E. A., Ahmed, S. E., Nelson, T., & Nathoo, F. S. (2020). Parameter and Mixture Component Estimation in Spatial Hidden Markov Models: A Comparative Analysis of Computational Methods. In J. Xu, G. Duca, S. E. Ahmed, F. P. García Márquez, & A. Hajiyev (Eds.), Proceedings of the 14th International Conference on Management Science and Engineering Management, ICMSEM 2020 - Volume 1 (pp. 340-355). (Advances in Intelligent Systems and Computing; Vol. 1190 AISC). Springer. https://doi.org/10.1007/978-3-030-49829-0_25