Affordable Access

Publisher Website

Maximum a posteriori estimation for Markov chains based on Gaussian Markov random fields

Authors
Journal
Procedia Computer Science
1877-0509
Publisher
Elsevier
Publication Date
Volume
1
Issue
1
Identifiers
DOI: 10.1016/j.procs.2010.04.186
Keywords
  • Markov Chain
  • Gaussian Markov Random Field
  • Maximum A Posteriori
  • Cross Validation
Disciplines
  • Computer Science

Abstract

Abstract In this paper, we present a Gaussian Markov random field (GMRF) model for the transition matrices (TMs) of Markov chains (MCs) by assuming the existence of a neighborhood relationship between states, and develop the maximum a posteriori (MAP) estimators under different observation conditions. Unlike earlier work on TM estimation, our method can make full use of the similarity between different states to improve the estimated accuracy, and the estimator can be performed very efficiently by solving a convex programming problem. In addition, we discuss the parameter choice of the proposed model, and introduce a Monte Carlo cross validation (MCCV) method. The numerical simulations of a diffusion process are employed to show the effectiveness of the proposed models and algorithms.

There are no comments yet on this publication. Be the first to share your thoughts.