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Likelihood and Bayesian inferencewit...

Held, Leonhard.

Likelihood and Bayesian inferencewith applications in biology and medicine /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Likelihood and Bayesian inferenceby Leonhard Held, Daniel Sabanes Bove.
Reminder of title:	with applications in biology and medicine /
Author:	Held, Leonhard.
other author:	Sabanes Bove, Daniel.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg :2020.
Description:	xiii, 402 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Bayesian statistical decision theory.
Online resource:	https://doi.org/10.1007/978-3-662-60792-3
ISBN:	9783662607923$q(electronic bk.)

Likelihood and Bayesian inferencewith applications in biology and medicine /
Held, Leonhard.

Likelihood and Bayesian inferencewith applications in biology and medicine /[electronic resource] :by Leonhard Held, Daniel Sabanes Bove. - Second edition. - Berlin, Heidelberg :Springer Berlin Heidelberg :2020. - xiii, 402 p. :ill., digital ;24 cm. - Statistics for biology and health,1431-8776. - Statistics for biology and health..

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.

ISBN: 9783662607923$q(electronic bk.)

Standard No.: 10.1007/978-3-662-60792-3doiSubjects--Topical Terms:

182005
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .H453 2020

Dewey Class. No.: 519.542

Likelihood and Bayesian inferencewith applications in biology and medicine /
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520 $a 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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