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Likelihood and Bayesian Inference
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Likelihood and Bayesian Inference : With Applications in Biology and Medicine

2nd ed. 2020

by Daniel Sabanes Bove , Leonhard Held

Book Details

Format Paperback / Softback
Book Series Statistics for Biology and Health
ISBN-10 3662607948
ISBN-13 9783662607947
Edition 2nd ed. 2020
Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Country of Manufacture GB
Country of Publication GB
Publication Date Apr 1st, 2021
Print length 402 Pages
Weight 646 grams
Dimensions 15.60 x 23.60 x 2.60 cms
Product Classification: Bayesian inference Applied mathematics Life sciences: general issues
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Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods.
This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology. Firstly, it discusses the importance of statistical models in applied quantitative research and the central role of the likelihood function, describing likelihood-based inference from a frequentist viewpoint, and exploring the properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book "Applied Statistical Inference" has been expanded to include new material on Markov models for time series analysis. It also features a comprehensive appendix covering the prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis, and each chapter is complemented by exercises. The text is primarily intended for graduate statistics and biostatistics students with an interest in applications.



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