Parametric families on large binary spaces
Schäfer, Christian (2011-02), Parametric families on large binary spaces. https://basepub.dauphine.fr/handle/123456789/5718
TypeDocument de travail / Working paper
External document linkhttp://hal.archives-ouvertes.fr/hal-00507420/fr/
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Abstract (EN)In the context of adaptive Monte Carlo algorithms, we cannot directly generate independent samples from the distribution of interest but use a proxy which we need to be close to the target. Generally, such a proxy distribution is a parametric family on the sampling spaces of the target distribution. For continuous sampling problems in high dimensions, we often use the multivariate normal distribution as a proxy for we can easily parametrise it by its moments and quickly sample from it. Our objective is to construct similarly flexible parametric families on binary sampling spaces too large for exhaustive enumeration. The binary sampling problem is more difficult than its continuous counterpart since the choice of a suitable proxy distribution is not obvious.
Subjects / KeywordsMultivariate binary data; Binary proposal distributions; Adaptive Monte Carlo; Binary parametric families
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Schäfer, Christian (2012-11) Thèse
Schäfer, Christian (2011) Document de travail / Working paper