Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting

Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting (2006)

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by Martin J. Wainwright , Max Chickering

Venue:	Journal of Machine Learning Research
Citations:	25 - 2 self

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@TECHREPORT{Wainwright06estimatingthe,
    author = {Martin J. Wainwright and Max Chickering},
    title = {Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting},
    institution = {Journal of Machine Learning Research},
    year = {2006}
}

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Abstract

Consider the problem of joint parameter estimation and prediction in a Markov random field: that is, the model parameters are estimated on the basis of an initial set of data, and then the fitted model is used to perform prediction (e.g., smoothing, denoising, interpolation) on a new noisy observation.

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