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• W2099667085 abstract "We consider the problem of approximate inference in the context of Bayesian decision theory. Traditional approaches focus on approximating general properties of the posterior, ignoring the decision task { and associated losses { for which the posterior could be used. We argue that this can be suboptimal and propose instead to loss-calibrate the approximate inference methods with respect to the decision task at hand. We present a general framework rooted in Bayesian decision theory to analyze approximate inference from the perspective of losses, opening up several research directions. As a rst loss-calibrated approximate inference attempt, we propose an EM-like algorithm on the Bayesian posterior risk and show how it can improve a standard approach to Gaussian process classication when losses are asymmetric." @default.
• W2099667085 created "2016-06-24" @default.
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• W2099667085 date "2011-06-14" @default.
• W2099667085 modified "2023-09-26" @default.
• W2099667085 title "Approximate inference for the loss-calibrated Bayesian" @default.
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