Recursive Pathways to Marginal Likelihood Estimation with Prior-Sensitivity AnalysisReport as inadecuate



 Recursive Pathways to Marginal Likelihood Estimation with Prior-Sensitivity Analysis


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We investigate the utility to computational Bayesian analyses of a particular family of recursive marginal likelihood estimators characterized by the equivalent algorithms known as -biased sampling- or -reverse logistic regression- in the statistics literature and -the density of states- in physics. Through a pair of numerical examples including mixture modeling of the well-known galaxy dataset we highlight the remarkable diversity of sampling schemes amenable to such recursive normalization, as well as the notable efficiency of the resulting pseudo-mixture distributions for gauging prior-sensitivity in the Bayesian model selection context. Our key theoretical contributions are to introduce a novel heuristic -thermodynamic integration via importance sampling- for qualifying the role of the bridging sequence in this procedure, and to reveal various connections between these recursive estimators and the nested sampling technique.



Author: Ewan Cameron; Anthony Pettitt

Source: https://archive.org/







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