Applied Bayesian modelling / Peter Congdon.Material type: TextSeries: Wiley series in probability and statisticsPublisher: Chichester, West Sussex : John Wiley & Sons, 2014Edition: Second editionDescription: 1 online resource (ix, 437 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9781118895061; 1118895061; 9781118895054; 1118895053; 9781118895047; 1118895045Subject(s): Bayesian statistical decision theory | Mathematical statistics | Bayesian statistical decision theory | Mathematical statistics | MATHEMATICS -- Applied | MATHEMATICS -- Probability & Statistics -- General | Bayesian statistical decision theory | Mathematical statistics | Bayesian statistical decision theory | Mathematical statisticsGenre/Form: Electronic books. | Electronic books.Additional physical formats: Print version:: Applied Bayesian modelling.DDC classification: 519.5/42 LOC classification: QA279.5Online resources: Click here to access online
Includes bibliographical references and index.
Bayesian methods and Bayesian estimation -- Hierarchical models for related units -- Regression techniques -- More advanced regression techniques -- Meta-analysis and multilevel models -- Models for time series -- Analysis of panel data -- Models for spatial outcomes and geographical association -- Latent variable and structural equation models -- Survival and event history models.
Print version record and CIP data provided by publisher.
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using Win.