The neighborhood MCMC sampler for learning Bayesian networks

Salem A. Alyami, A. K. M. Azad, Jonathan Keith

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) structures. In this paper, we studied a recently proposed Markov chain Monte Carlo (MCMC) sampler, called the Neighbourhood sampler (NS), and examined how efficiently it can sample BNs when local maxima are present. We assume that a posterior distribution f(N,E|D) has been defined, where D represents data relevant to the inference, N and E are the sets of nodes and directed edges, respectively. We illustrate the new approach by sampling from such a distribution, and inferring BNs. The simulations conducted in this paper show that the new learning approach substantially avoids getting stuck in local modes of the distribution, and achieves a more rapid rate of convergence, compared to other common algorithms e.g. the MCMC Metropolis-Hastings sampler.

Original languageEnglish
Title of host publicationProceedings of SPIE
Subtitle of host publicationFirst International Workshop on Pattern Recognition
EditorsXudong Jiang, Guojian Chen, Genci Capi, Chiharu Ishii
Place of PublicationBellingham WA
PublisherSPIE
Number of pages11
ISBN (Electronic)9781510604315
ISBN (Print)9781510604308
DOIs
Publication statusPublished - 11 Jul 2016
EventInternational Workshop on Pattern Recognition (IWPR 2016) - Hotel Sunroute Plaza Shinjuku, Tokyo, Japan
Duration: 11 May 201613 May 2016
Conference number: 1st
http://www.icopr.org/
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10011/1/Front-Matter-Volume-10011/10.1117/12.2248688.full

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume10011
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Workshop

WorkshopInternational Workshop on Pattern Recognition (IWPR 2016)
Abbreviated titleIWPR 2016
CountryJapan
CityTokyo
Period11/05/1613/05/16
Internet address

Keywords

  • Directed acyclic graph
  • Graph space
  • Local maxima
  • Structure inference

Cite this

Alyami, S. A., Azad, A. K. M., & Keith, J. (2016). The neighborhood MCMC sampler for learning Bayesian networks. In X. Jiang, G. Chen, G. Capi, & C. Ishii (Eds.), Proceedings of SPIE: First International Workshop on Pattern Recognition [100111K] (Proceedings of SPIE; Vol. 10011). Bellingham WA: SPIE. https://doi.org/10.1117/12.2242708