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Maximum a posteriori density estimation and the sparse grid combination technique

Wong, Matthias; Hegland, Markus


We study a novel method for maximum a posteriori (map) estimation of the probability density function of an arbitrary, independent and identically distributed d-dimensional data set. We give an interpretation of the map algorithm in terms of regularised maximum likelihood. We also present numerical experiments using a sparse grid combination technique and the 'opticom' method. The numerical results demonstrate the viability of parallelisation for the combination technique.

CollectionsANU Research Publications
Date published: 2013
Type: Journal article
Source: ANZIAM Journal


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