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Likelihood and Bayesian Inference

With Applications in Biology and Medicine

  • Textbook
  • © 2020

Overview

Authors:
  1. Leonhard Held

    1. Epidemiology, Biostatistics & Prevention, University of Zurich, Zürich, Switzerland

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    You can also search for this author in PubMed   Google Scholar

  2. Daniel Sabanés Bové

    1. Inst. of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Offers an easily accessible and comprehensive introduction to model-based statistical inference
  • Provides real-world applications in biology, medicine and epidemiology with programming examples in the open-source software R
  • Includes exercises at the end of each chapter, for which solutions are available on the website
  • Includes a comprehensive appendix covering the necessary mathematical background and various R-programming tips

Part of the book series: Statistics for Biology and Health (SBH)

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Table of contents (10 chapters)

  1. Front Matter

    Pages I-XIII
    Download chapter PDF

  2. Introduction

    • Leonhard Held, Daniel Sabanés Bové
    Pages 1-12

  3. Likelihood

    • Leonhard Held, Daniel Sabanés Bové
    Pages 13-50

  4. Elements of Frequentist Inference

    • Leonhard Held, Daniel Sabanés Bové
    Pages 51-78

  5. Frequentist Properties of the Likelihood

    • Leonhard Held, Daniel Sabanés Bové
    Pages 79-122

  6. Likelihood Inference in Multiparameter Models

    • Leonhard Held, Daniel Sabanés Bové
    Pages 123-165

  7. Bayesian Inference

    • Leonhard Held, Daniel Sabanés Bové
    Pages 167-219

  8. Model Selection

    • Leonhard Held, Daniel Sabanés Bové
    Pages 221-245

  9. Numerical Methods for Bayesian Inference

    • Leonhard Held, Daniel Sabanés Bové
    Pages 247-287

  10. Prediction

    • Leonhard Held, Daniel Sabanés Bové
    Pages 289-314

  11. Markov Models for Time Series Analysis

    • Leonhard Held, Daniel Sabanés Bové
    Pages 315-342

  12. Back Matter

    Pages 343-402
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Keywords

About this book

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.



Reviews

“If you need a guidebook to follow when you need to refresh past statistical concepts from your memory, or even learn the rationale behind a method you are not familiar with, this user-friendly book will give you a perfect starting point.” (Pablo Hernández-Alonso, ISCB News, iscb.info, June, 2022)

Authors and Affiliations

  • Epidemiology, Biostatistics & Prevention, University of Zurich, Zürich, Switzerland

    Leonhard Held

  • Inst. of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland

    Daniel Sabanés Bové

About the authors

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.


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