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Likelihood and Bayesian Inference
Af: Daniel Sabanes Bove, Leonhard Held Engelsk Paperback
SPAR
kr 117
Likelihood and Bayesian Inference
Af: Daniel Sabanes Bove, Leonhard Held Engelsk Paperback
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.



Produktdetaljer
Sprog: Engelsk
Sider: 402
ISBN-13: 9783662607947
Indbinding: Paperback
Udgave:
ISBN-10: 3662607948
Udg. Dato: 1 apr 2021
Længde: 26mm
Bredde: 156mm
Højde: 236mm
Oplagsdato: 1 apr 2021
Forfatter(e) Daniel Sabanes Bove, Leonhard Held


Kategori Bayesiansk statistik


Sprog Engelsk


Indbinding Paperback


Sider 402


Udgave


Længde 26mm


Bredde 156mm


Højde 236mm


Udg. Dato 1 apr 2021


Oplagsdato 1 apr 2021

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