Likelihood and Bayesian Inference

Leonhard Held|Daniel Sabanés Bové

ISBN 10: 3662607913 / ISBN 13: 9783662607916

Published by Springer Berlin Heidelberg, 2020

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers an easily accessible and comprehensive introduction to model-based statistical inferenceProvides real-world applications in biology, medicine and epidemiology with programming examples in the open-source software RIncludes exe.

Seller Inventory # 449139973

Bibliographic Details

• Title: Likelihood and Bayesian Inference
• Publisher: Springer Berlin Heidelberg
• Publication Date: 2020
• Language: English
• ISBN 10: 3662607913
• ISBN 13: 9783662607916
• Binding: Gebunden
• Edition: 2nd Edition
• Condition: New
• Seller catalogs:
  • Mathematik/Naturwissenschaften/Technik/Medizin

About this title

Synopsis

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.

About the Author

Leonhard Held is a Full Professor of Biostatistics, Director of the Master’s Program in Biostatistics and Chair of the Center for Reproducible Science at the University of Zurich, Switzerland. He has published several books and numerous articles on statistical methodology, applied statistics and biomedical research and teaches undergraduate and graduate-level courses in Biostatistics and Medical Statistics.

Daniel Sabanés Bové completed his PhD in Statistics at the University of Zurich under the supervision of Leonhard Held. He started his career as a biostatistician in oncology drug development at Hoffmann-La Roche in 2013, and has been a data scientist at Google since 2018.