Bayesian Nonparametrics (Cambridge Series in Statistical and by Nils Lid Hjort,Chris Holmes,Peter Müller,Stephen G. Walker

By Nils Lid Hjort,Chris Holmes,Peter Müller,Stephen G. Walker

Bayesian nonparametrics works - theoretically, computationally. the idea presents hugely versatile versions whose complexity grows thoroughly with the quantity of knowledge. Computational matters, notwithstanding difficult, aren't any longer intractable. All that's wanted is an access aspect: this clever booklet is the correct advisor to what can appear a forbidding panorama. educational chapters by means of Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson enhance from thought, to uncomplicated versions and hierarchical modeling, to functions and implementation, rather in computing device technology and biostatistics. those are complemented via spouse chapters through the editors and Griffin and Quintana, delivering extra versions, reading computational concerns, selecting destiny progress parts, and giving hyperlinks to similar subject matters. This coherent textual content offers prepared entry either to underlying ideas and to state of the art perform. particular examples are drawn from info retrieval, NLP, desktop imaginative and prescient, computational biology, biostatistics, and bioinformatics.

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