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Bayesian biostatistics /
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place p...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex :
Wiley,
2012.
|
| Colección: | Statistics in practice.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Lesaffre, Emmanuel. | |
| 245 | 1 | 0 | |a Bayesian biostatistics / |c Emmanuel Lesaffre, Andrew B. Lawson. |
| 260 | |a Chichester, West Sussex : |b Wiley, |c 2012. | ||
| 300 | |a 1 online resource | ||
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| 490 | 1 | |a Statistics in practice | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |6 880-01 |a Basic concepts in Bayesian methods -- Bayes theorem -- Posterior summary measures -- More than one parameter -- The prior distribution -- Markov chain Monte Carlo -- Software -- Hierarchical models -- Model building and assessment -- Variable selection -- Bioassay -- Measurement error -- Survival analysis -- Longitudinal analysis -- Disease mapping & image analysis -- Final chapter -- Distributions. | |
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 520 | |a The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introduc. | ||
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| 650 | 0 | |a Biometry |x Methodology. | |
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 2 | |a Biostatistics |x methods | |
| 650 | 2 | |a Bayes Theorem | |
| 650 | 6 | |a Biométrie |x Méthodologie. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 6 | |a Théorème de Bayes. | |
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| 650 | 7 | |a SCIENCE |x Life Sciences |x Biology. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Life Sciences |x General. |2 bisacsh | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 700 | 1 | |a Lawson, Andrew |q (Andrew B.) | |
| 758 | |i has work: |a Bayesian biostatistics (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGKp69VHvx44Dp6YMb33V3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Lesaffre, Emmanuel. |t Bayesian biostatistics. |d Chichester, West Sussex : John Wiley & Sons, 2012 |z 9781118321850 |w (DLC) 2012004237 |
| 830 | 0 | |a Statistics in practice. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=943817 |z Texto completo |
| 880 | 0 | |6 505-00/(S |a Bayesian Biostatistics -- Contents -- Preface -- Notation, terminology and some guidance for reading the book -- Part I BASIC CONCEPTS IN BAYESIAN METHODS -- 1 Modes of statistical inference -- 1.1 The frequentist approach: A critical reflection -- 1.1.1 The classical statistical approach -- 1.1.2 The P-value as a measure of evidence -- 1.1.3 The confidence interval as a measure of evidence -- 1.1.4 An historical note on the two frequentist paradigms -- 1.2 Statistical inference based on the likelihood function -- 1.2.1 The likelihood function -- 1.2.2 The likelihood principles -- 1.3 The Bayesian approach: Some basic ideas -- 1.3.1 Introduction -- 1.3.2 Bayes theorem -- discrete version for simple events -- 1.4 Outlook -- Exercises -- 2 Bayes theorem: Computing the posterior distribution -- 2.1 Introduction -- 2.2 Bayes theorem -- the binary version -- 2.3 Probability in a Bayesian context -- 2.4 Bayes theorem -- the categorical version -- 2.5 Bayes theorem -- the continuous version -- 2.6 The binomial case -- 2.7 The Gaussian case -- 2.8 The Poisson case -- 2.9 The prior and posterior distribution of h(θ) -- 2.10 Bayesian versus likelihood approach -- 2.11 Bayesian versus frequentist approach -- 2.12 The different modes of the Bayesian approach -- 2.13 An historical note on the Bayesian approach -- 2.14 Closing remarks -- Exercises -- 3 Introduction to Bayesian inference -- 3.1 Introduction -- 3.2 Summarizing the posterior by probabilities -- 3.3 Posterior summary measures -- 3.3.1 Characterizing the location and variability of the posterior distribution -- 3.3.2 Posterior interval estimation -- 3.4 Predictive distributions -- 3.4.1 The frequentist approach to prediction -- 3.4.2 The Bayesian approach to prediction -- 3.4.3 Applications -- 3.5 Exchangeability -- 3.6 A normal approximation to the posterior. | |
| 880 | 8 | |6 505-01/(S |a 3.6.1 A Bayesian analysis based on a normal approximation to the likelihood -- 3.6.2 Asymptotic properties of the posterior distribution -- 3.7 Numerical techniques to determine the posterior -- 3.7.1 Numerical integration -- 3.7.2 Sampling from the posterior -- 3.7.3 Choice of posterior summary measures -- 3.8 Bayesian hypothesis testing -- 3.8.1 Inference based on credible intervals -- 3.8.2 The Bayes factor -- 3.8.3 Bayesian versus frequentist hypothesis testing -- 3.9 Closing remarks -- Exercises -- 4 More than one parameter -- 4.1 Introduction -- 4.2 Joint versus marginal posterior inference -- 4.3 The normal distribution with μ and σ2 unknown -- 4.3.1 No prior knowledge on μ and σ2 is available -- 4.3.2 An historical study is available -- 4.3.3 Expert knowledge is available -- 4.4 Multivariate distributions -- 4.4.1 The multivariate normal and related distributions -- 4.4.2 The multinomial distribution -- 4.5 Frequentist properties of Bayesian inference -- 4.6 Sampling from the posterior distribution: The Method of Composition -- 4.7 Bayesian linear regression models -- 4.7.1 The frequentist approach to linear regression -- 4.7.2 A noninformative Bayesian linear regression model -- 4.7.3 Posterior summary measures for the linear regression model -- 4.7.4 Sampling from the posterior distribution -- 4.7.5 An informative Bayesian linear regression model -- 4.8 Bayesian generalized linear models -- 4.9 More complex regression models -- 4.10 Closing remarks -- Exercises -- 5 Choosing the prior distribution -- 5.1 Introduction -- 5.2 The sequential use of Bayes theorem -- 5.3 Conjugate prior distributions -- 5.3.1 Univariate data distributions -- 5.3.2 Normal distribution -- mean and variance unknown -- 5.3.3 Multivariate data distributions -- 5.3.4 Conditional conjugate and semiconjugate distributions -- 5.3.5 Hyperpriors. | |
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