Published by CRC Press, Boca Raton, 2018
ISBN 10: 1498727255 ISBN 13: 9781498727259
Language: English
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Published by Taylor & Francis Ltd, 2020
ISBN 10: 0367572265 ISBN 13: 9780367572266
Language: English
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Published by Taylor & Francis Group, 2018
ISBN 10: 1498727255 ISBN 13: 9781498727259
Language: English
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Published by Taylor & Francis Group, 2018
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Language: English
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Published by Chapman and Hall/CRC, 2018
ISBN 10: 1498727255 ISBN 13: 9781498727259
Language: English
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Published by Taylor & Francis Group, 2018
ISBN 10: 1498727255 ISBN 13: 9781498727259
Language: English
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Add to basketPaperback. Condition: Brand New. 324 pages. 9.21x6.14x0.71 inches. In Stock.
Published by Chapman and Hall/CRC, 2018
ISBN 10: 1498727255 ISBN 13: 9781498727259
Language: English
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Published by Chapman and Hall/CRC, 2018
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Language: English
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Published by Chapman and Hall/CRC, 2018
ISBN 10: 1498727255 ISBN 13: 9781498727259
Language: English
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Published by Taylor & Francis Inc, 2018
ISBN 10: 1498727255 ISBN 13: 9781498727259
Language: English
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Published by Chapman and Hall/CRC, 2018
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Add to basketHardcover. Condition: Brand New. 312 pages. 9.25x6.25x1.00 inches. In Stock.
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Add to basketGebunden. Condition: New. INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient.
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Add to basketBuch. Condition: Neu. Neuware - This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms, which come with a range of issues that impede practical use of Bayesian models.
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Add to basketPaperback. Condition: Brand New. 324 pages. 9.21x6.14x0.71 inches. In Stock. This item is printed on demand.
Published by Chapman And Hall/CRC, 2020
ISBN 10: 0367572265 ISBN 13: 9780367572266
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference.Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download.The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work.Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic.Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York.Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient.