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Bayesian Statistics An Introduction.

Detalles Bibliográficos
Autor principal: Lee, Peter M.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2012.
Colección:New York Academy of Sciences Ser.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Lee, Peter M. 
245 1 0 |a Bayesian Statistics  |h [electronic resource] :  |b An Introduction. 
260 |a Newark :  |b John Wiley & Sons, Incorporated,  |c 2012. 
300 |a 1 online resource (488 p.). 
490 1 |a New York Academy of Sciences Ser. 
500 |a Description based upon print version of record. 
505 0 |a Intro -- Bayesian Statistics -- Contents -- Preface -- Preface to the First Edition -- 1 Preliminaries -- 1.1 Probability and Bayes' Theorem -- 1.1.1 Notation -- 1.1.2 Axioms for probability -- 1.1.3 'Unconditional' probability -- 1.1.4 Odds -- 1.1.5 Independence -- 1.1.6 Some simple consequences of the axioms -- Bayes' Theorem -- 1.2 Examples on Bayes' Theorem -- 1.2.1 The Biology of Twins -- 1.2.2 A political example -- 1.2.3 A warning -- 1.3 Random variables -- 1.3.1 Discrete random variables -- 1.3.2 The binomial distribution -- 1.3.3 Continuous random variables 
505 8 |a 1.3.4 The normal distribution -- 1.3.5 Mixed random variables -- 1.4 Several random variables -- 1.4.1 Two discrete random variables -- 1.4.2 Two continuous random variables -- 1.4.3 Bayes' Theorem for random variables -- 1.4.4 Example -- 1.4.5 One discrete variable and one continuous variable -- 1.4.6 Independent random variables -- 1.5 Means and variances -- 1.5.1 Expectations -- 1.5.2 The expectation of a sum and of a product -- 1.5.3 Variance, precision and standard deviation -- 1.5.4 Examples -- 1.5.5 Variance of a sum -- covariance and correlation 
505 8 |a 1.5.6 Approximations to the mean and variance of a function of a random variable -- 1.5.7 Conditional expectations and variances -- 1.5.8 Medians and modes -- 1.6 Exercises on Chapter 1 -- 2 Bayesian inference for the normal distribution -- 2.1 Nature of Bayesian inference -- 2.1.1 Preliminary remarks -- 2.1.2 Post is prior times likelihood -- 2.1.3 Likelihood can be multiplied by any constant -- 2.1.4 Sequential use of Bayes' Theorem -- 2.1.5 The predictive distribution -- 2.1.6 A warning -- 2.2 Normal prior and likelihood -- 2.2.1 Posterior from a normal prior and likelihood -- 2.2.2 Example 
505 8 |a 2.2.3 Predictive distribution -- 2.2.4 The nature of the assumptions made -- 2.3 Several normal observations with a normal prior -- 2.3.1 Posterior distribution -- 2.3.2 Example -- 2.3.3 Predictive distribution -- 2.3.4 Robustness -- 2.4 Dominant likelihoods -- 2.4.1 Improper priors -- 2.4.2 Approximation of proper priors by improper priors -- 2.5 Locally uniform priors -- 2.5.1 Bayes' postulate -- 2.5.2 Data translated likelihoods -- 2.5.3 Transformation of unknown parameters -- 2.6 Highest density regions -- 2.6.1 Need for summaries of posterior information 
505 8 |a 2.6.2 Relation to classical statistics -- 2.7 Normal variance -- 2.7.1 A suitable prior for the normal variance -- 2.7.2 Reference prior for the normal variance -- 2.8 HDRs for the normal variance -- 2.8.1 What distribution should we be considering? -- 2.8.2 Example -- 2.9 The role of sufficiency -- 2.9.1 Definition of sufficiency -- 2.9.2 Neyman's factorization theorem -- 2.9.3 Sufficiency principle -- 2.9.4 Examples -- 2.9.5 Order statistics and minimal sufficient statistics -- 2.9.6 Examples on minimal sufficiency -- 2.10 Conjugate prior distributions -- 2.10.1 Definition and difficulties 
500 |a 2.10.2 Examples 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
655 0 |a Electronic books. 
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776 0 8 |i Print version:  |a Lee, Peter M.  |t Bayesian Statistics  |d Newark : John Wiley & Sons, Incorporated,c2012  |z 9781118332573 
830 0 |a New York Academy of Sciences Ser. 
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