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Maximum a posteriori estimation
Method of estimating the parameters of a statistical model
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An estimation procedure that is often claimed to be part of Bayesian statistics is the maximum a posteriori (MAP) estimate of an unknown quantity, that equals the mode of the posterior density with respect to some reference measure, typically the Lebesgue measure. The MAP can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data. It is closely related to the method of maximum likelihood (ML) estimation, but employs an augmented optimization objective which incorporates a prior density over the quantity one wants to estimate.
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🇺🇸 Exact MAP inference in general higher-order graphical models using linear programming
arXiv:1709.09051v2 Announce Type: replace-cross Abstract: This paper is concerned with the problem of exact MAP inference in general higher-order gra...
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