Minimum Conditional Description Length Estimation for Markov Random Fields
dc.contributor.author | Reyes, Matthew | |
dc.contributor.author | Neuhoff, David | |
dc.date.accessioned | 2016-02-23T21:44:27Z | |
dc.date.available | 2016-02-23T21:44:27Z | |
dc.date.issued | 2016-02-23 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/117383 | |
dc.description.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 $U\subset V$, 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. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Markov fields, parameter estimation, minimum description length, pseudo-likelihood | en_US |
dc.title | Minimum Conditional Description Length Estimation for Markov Random Fields | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Electrical Engineering | |
dc.subject.hlbsecondlevel | Computer Science | |
dc.subject.hlbtoplevel | Engineering | |
dc.contributor.affiliationum | Electrical Engineering and Computer Science, Department of | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/117383/1/mgreyes_dlneuhoff_ITA_MCDL_deepblue.pdf | |
dc.description.filedescription | Description of mgreyes_dlneuhoff_ITA_MCDL_deepblue.pdf : article | |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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