期刊
ANNALS OF STATISTICS
卷 34, 期 1, 页码 123-145出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/009053605000000912
关键词
Markov random field; pseudo-likelihood; Gibbs measure; model selection; information criterion; typicality
For Markov random fields on Z(d) with finite state space, we address the statistical estimation of the basic neighborhood, the smallest region that determines the conditional distribution at a site on the condition that the values at all other sites are given. A modification of the Bayesian Information Criterion, replacing likelihood by pseudo-likelihood, is proved to provide strongly consistent estimation from observing a realization of the field on increasing finite regions: the estimated basic neighborhood equals the true one eventually almost surely, not assuming any prior bound on the size of the latter. Stationarity of the Markov field is not required, and phase transition does not affect the results.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据