Minimum Conditional Description Length Estimation for Markov Random Fields

被引：0

作者：

Reyes, Matthew G. ^{[1
]}

Neuhoff, David L. ^{[1
]}

机构：

[1] Univ Michigan, Dept EECS, Ann Arbor, MI 48109 USA

来源：

2016 INFORMATION THEORY AND APPLICATIONS WORKSHOP (ITA) | 2016年

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper we discuss a method, which we call Minimum Conditional Description Length (MCDL), for estimating the parameters of a subset of sites within a Markov random field. We assume that the edges are known for the entire graph G = (V, E). Then, for a subset U.V, we estimate the parameters for nodes in U as well as for edges incident to a node in U, by finding the exponential parameter for that subset that yields the best compression conditioned on the values on the boundary. U. Our estimate is derived from a temporally stationary sequence of observations on the set U. We discuss how this method can also be applied to estimate a spatially invariant parameter from a single configuration, and in so doing, derive the Maximum Pseudo-Likelihood (MPL) estimate.

引用

页数：6

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