An Introduction to Bayesian Inference, Methods and Computation.
Heard, Nick
ISBN 10: 3030828077 ISBN 13: 9783030828073
Published by Cham, Springer., 2021
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Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
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About this Item
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XII, 169 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch. Seller Inventory # 6916BB
Synopsis:
These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.
About the Author:
Professor Nick Heard received his PhD degree from the Department of Mathematics at Imperial College London in 2001 and currently holds the position of Chair in Statistics at Imperial. His research interests include developing statistical models for cyber-security applications, finding community structure in large dynamic networks, clustering and changepoint analysis, in each case using computational Bayesian methods.
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Bibliographic Details
Title: An Introduction to Bayesian Inference, ...
Publisher: Cham, Springer.
Publication Date: 2021
Binding: Hardcover
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Buch. Condition: Neu. Neuware -These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 184 pp. Englisch. Seller Inventory # 9783030828073
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