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＜電子ブック＞
Independent Random Sampling Methods / by Luca Martino, David Luengo, Joaquín Míguez
(Statistics and Computing. ISSN:21971706)

版	1st ed. 2018.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2018
大きさ	XII, 280 p. 55 illus., 52 illus. in color : online resource
著者標目	*Martino, Luca author Luengo, David author Míguez, Joaquín author SpringerLink (Online service)
件　名	LCSH:Mathematical statistics—Data processing LCSH:Computer science—Mathematics LCSH:Social sciences—Mathematics LCSH:Statistics  FREE:Statistics and Computing FREE:Mathematics of Computing FREE:Mathematics in Business, Economics and Finance FREE:Statistical Theory and Methods FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
一般注記	Introduction -- Direct methods -- Accept-Reject methods -- Adaptive rejection sampling methods -- Ratio of Uniforms -- Independent sampling for multivariate densities -- Asymptotically independent samplers -- Summary and outlook -- A. Acronyms and abbrevations -- B. Notation -- C. Jones' RoU generalization -- D. Polar transformation This book systematically addresses the design and analysis of efficient techniques for independent random sampling. Both general-purpose approaches, which can be used to generate samples from arbitrary probability distributions, and tailored techniques, designed to efficiently address common real-world practical problems, are introduced and discussed in detail. In turn, the monograph presents fundamental results and methodologies in the field, elaborating and developing them into the latest techniques. The theory and methods are illustrated with a varied collection of examples, which are discussed in detail in the text and supplemented with ready-to-run computer code. The main problem addressed in the book is how to generate independent random samples from an arbitrary probability distribution with the weakest possible constraints or assumptions in a form suitable for practical implementation. The authors review the fundamental results and methods in the field, address the latest methods, and emphasize the links and interplay between ostensibly diverse techniques HTTP:URL=https://doi.org/10.1007/978-3-319-72634-2

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