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Likelihood and Bayesian Inference

- With Applications in Biology and Medicine
Af: Daniel Sabanes Bove, Leonhard Held Engelsk Paperback

Likelihood and Bayesian Inference

- With Applications in Biology and Medicine
Af: Daniel Sabanes Bove, Leonhard Held Engelsk Paperback
Tjek vores konkurrenters priser
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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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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Produktdetaljer
Sprog: Engelsk
Sider: 402
ISBN-13: 9783662607947
Indbinding: Paperback
Udgave:
ISBN-10: 3662607948
Kategori: Bayesiansk statistik
Udg. Dato: 1 apr 2021
Længde: 26mm
Bredde: 236mm
Højde: 156mm
Forlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Oplagsdato: 1 apr 2021
Forfatter(e): Daniel Sabanes Bove, Leonhard Held
Forfatter(e) Daniel Sabanes Bove, Leonhard Held

Kategori Bayesiansk statistik

ISBN-13 9783662607947

Sprog Engelsk

Indbinding Paperback

Sider 402

Udgave

Længde 26mm

Bredde 236mm

Højde 156mm

Udg. Dato 1 apr 2021

Oplagsdato 1 apr 2021

Forlag Springer-Verlag Berlin and Heidelberg GmbH & Co. KG