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• W2253722015 abstract "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 $Usubset V$, we estimate the parameters for nodes and edges 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 $partial U$. 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." @default.
• W2253722015 created "2016-06-24" @default.
• W2253722015 creator A5038249161 @default.
• W2253722015 creator A5053858700 @default.
• W2253722015 date "2016-02-09" @default.
• W2253722015 modified "2023-09-27" @default.
• W2253722015 title "Minimum Conditional Description Length Estimation for Markov Random Fields" @default.
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