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An Adaptive Derivative Free Method for Bayesian Posterior Approximation

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Details

Original languageEnglish
Article number12436433
Pages (from-to)87-90
JournalIEEE Signal Processing Letters
Volume19
Issue number2
DOIs
Publication statusPublished - 2012
Publication typeA1 Journal article-refereed

Abstract

In the Gaussian mixture approach a Bayesian posterior probability distribution function is approximated using a weighted sum of Gaussians. This work presents a novel method for generating a Gaussian mixture by splitting the prior taking the direction of maximum nonlinearity into account. The proposed method is computationally feasible and does not require analytical differentiation. Tests show that the method approximates the posterior better with fewer Gaussian components than existing methods.

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Field of science, Statistics Finland

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