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Record Nr. |
UNINA9910484137403321 |
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Autore |
Held Leonhard |
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Titolo |
Likelihood and bayesian Inference : with applications in biology and medicine / / by Leonhard Held, Daniel Sabanés Bové |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer-Verlag, , 2020 |
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ISBN |
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9783662607923 |
3-662-60792-1 |
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Edizione |
[Second edition] |
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Descrizione fisica |
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1 online resource (XIII, 402 pages, 84 illustrations) |
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Collana |
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Statistics for Biology and Health, , 2197-5671 |
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Disciplina |
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Soggetti |
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Biometry |
Statistics |
Ecology |
Population genetics |
Biostatistics |
Statistical Theory and Methods |
Bayesian Inference |
Theoretical and Statistical Ecology |
Population Genetics |
Estadística bayesiana |
Estadística matemàtica |
Biometria |
Estadística mèdica |
Probabilitats |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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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 |
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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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