| Dewey Class |
519.5 |
| Title |
Likelihood and Bayesian Inference ([EBook]) : With Applications in Biology and Medicine / / by Leonhard Held, Daniel Sabanés Bové. |
| Author |
Held, Leonhard |
| Added Personal Name |
Sabanés Bové, Daniel |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
2nd ed. 2020. |
| Publication |
Berlin, Heidelberg : : Springer Berlin Heidelberg : : Imprint: Springer, , 2020. |
| Physical Details |
XIII, 402 p. 84 illus. : online resource. |
| Series |
Statistics for Biology and Health 1431-8776 |
| ISBN |
9783662607923 |
| Summary Note |
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.: |
| Mode of acces to digital resource |
Digital book. Cham Springer Nature 2020. - Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format |
| System details note |
- Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-3-662-60792-3 |
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