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GENERALIZED EXTREME VALUE ADDITIVE MODEL ANALYSIS VIA MEAN FIELD VARIATIONAL BAYES
Author(s) -
Neville Sarah E.,
Palmer M. J.,
Wand M. P.
Publication year - 2011
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2011.00637.x
Subject(s) - markov chain monte carlo , mathematics , bayes' theorem , bayesian inference , monte carlo method , bayesian probability , inference , statistics , extreme value theory , markov chain , bayes factor , metropolis–hastings algorithm , computer science , artificial intelligence
Summary We develop Mean Field Variational Bayes methodology for fast approximate inference in Bayesian Generalized Extreme Value additive model analysis. Such models are useful for flexibly assessing the impact of continuous predictor variables on sample extremes. The new methodology allows large Bayesian models to be fitted and assessed without the significant computing costs of Markov Chain Monte Carlo methods. We illustrate our new methodology with maximum rainfall data from the Sydney, Australia, hinterland. Comparisons are made between the Mean Field Variational Bayes and Markov Chain Monte Carlo approaches.