Bayesian data analysis / 
Andrew Gelman ... [et al.]. 
 - 2nd ed. 
 - Boca Raton, Fla. ;  London :  Chapman & Hall/CRC,  2003. 
 - 640 p. :  ill. ;  23 cm. 
 - Texts in statistical science .
 - Texts in statistical science .
<br/><br/> Previous ed.: London: Chapman & Hall, 1995. 
<br/><br/>Includes bibliographical references (p. 611-646) and indexes. 
<br/><br/>Part I: Fundamentals of Bayesian inference -- 1.Background -- 2.Single-parameter models-- 3.Introduction to multiparameter models -- 4.Large-sample inference and frequency properties of Bayesian inferences -- Part II.Fundamentals of Bayesian data analysis -- 5.Herarchical models -- 6.Model checking and improvement -- 7.Modeling accounting for data collection -- 8.Connects and challenges -- 9.General advice -- Part III.Advanced computation -- 10.Overview of computation -- 11.Posterior simulation  -- 12.Approximations based on posterior models -- 13.Special topics in computation -- Part IV: Regresson models -- 14.Introduction to regression modles -- 15.Hierarchical linear modles -- 16.Generalized linear models -- 17.Models for robust inference -- Part V.Specific models and problems -- 18.Mixture models -- 19.Multivariate models -- 20.Nonliner models -- 21.Models for missing data -- 22.Decision analysis. 
<br/><br/> Emphasising practice over theory this second edition incorporates new material on how Bayesian methods are connected to other approaches. It features a stronger focus upon MCMC, more examples and an added chapter on further computation topics. 
<br/><br/><label>ISBN: </label>9781584883883 (hbk.) :  £38.99 9781584883883 
<br/><br/><label>LCCN: </label>2003051474 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Bayesian statistical decision theory.<br/>Mathematics.<br/>Probability & statistics<br/>Maths for scientists<br/>Maths for engineers
<br/><br/><label>LC Class. No.: </label>QA279.5 
<br/><br/><label>Dewey Class. No.: </label>519.542 GEL