ScienceDirect® Home Skip Main Navigation Links
You have guest access to ScienceDirect. Find out more.
 
Home
Browse
My Settings
Alerts
Help
 Quick Search
 Search tips (Opens new window)
    Clear all fields    
advertisementadvertisement
International Journal of Approximate Reasoning
Volume 46, Issue 2, October 2007, Pages 300-319
Special Track on Uncertain Reasoning of the 18th International Florida Artificial Intelligence Research Symposium (FLAIRS 2005)
 
Font Size: Decrease Font Size  Increase Font Size
 Abstract - selected
Purchase PDF (693 K)

  E-mail Article   
  Add to my Quick Links   
Bookmark and share in 2collab (opens in new window)
Request permission to reuse this article
  Cited By in Scopus (0)
 
 
 
Related Articles in ScienceDirect
View More Related Articles
 
View Record in Scopus
 
doi:10.1016/j.ijar.2006.09.011    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Elsevier Inc. All rights reserved.

Convergence in Markovian models with implications for efficiency of inferencestar, open

Theodore CharitosCorresponding Author Contact Information, a, E-mail The Corresponding Author, Peter R. de Waala, E-mail The Corresponding Author and Linda C. van der Gaaga, E-mail The Corresponding Author

aDepartment of Information and Computing Sciences, Utrecht University, P.O. Box 80.089, 3508 TB Utrecht, The Netherlands

Received 30 June 2005; 
revised 26 June 2006; 
accepted 21 September 2006. 
Available online 17 November 2006.

Purchase the full-text article



References and further reading may be available for this article. To view references and further reading you must purchase this article.

Abstract

Sequential statistical models such as dynamic Bayesian networks and hidden Markov models more specifically, model stochastic processes over time. In this paper, we study for these models the effect of consecutive similar observations on the posterior probability distribution of the represented process. We show that, given such observations, the posterior distribution converges to a limit distribution. Building upon the rate of the convergence, we further show that, given some wished-for level of accuracy, part of the inference can be forestalled. To evaluate our theoretical results, we study their implications for a real-life model from the medical domain and for a benchmark model for agricultural purposes. Our results indicate that whenever consecutive similar observations arise, the computational requirements of inference in Markovian models can be drastically reduced.

Keywords: Markovian models; Consecutive similar observations; Convergence; Inference; Efficiency


International Journal of Approximate Reasoning
Volume 46, Issue 2, October 2007, Pages 300-319
Special Track on Uncertain Reasoning of the 18th International Florida Artificial Intelligence Research Symposium (FLAIRS 2005)
 
Home
Browse
My Settings
Alerts
Help
Elsevier.com (Opens new window)
About ScienceDirect  |  Contact Us  |  Information for Advertisers  |  Terms & Conditions  |  Privacy Policy
Copyright © 2008 Elsevier B.V. All rights reserved. ScienceDirect® is a registered trademark of Elsevier B.V.