A First Course in Bayesian Statistical Methods [electronic resource] / by Peter D. Hoff.
By: Hoff, Peter D [author.].
Contributor(s): SpringerLink (Online service).
Material type: TextSeries: Springer Texts in Statistics: Publisher: New York, NY : Springer New York : Imprint: Springer, 2009Description: X, 272 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387924076.Subject(s): Distribution (Probability theory  Mathematical statistics  Social sciences  Methodology  Computer science  Econometrics  Probability Theory and Stochastic Processes  Operations Research, Management Science  Statistical Theory and Methods  Methodology of the Social Sciences  Probability and Statistics in Computer Science  EconometricsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 519.2 Online resources: Click here to access onlineItem type  Current location  Call number  Status  Date due  Barcode  Item holds  

EBOOKS 
ISI Library, Kolkata

Available  EB1622 
and examples  Belief, probability and exchangeability  Oneparameter models  Monte Carlo approximation  The normal model  Posterior approximation with the Gibbs sampler  The multivariate normal model  Group comparisons and hierarchical modeling  Linear regression  Nonconjugate priors and MetropolisHastings algorithms  Linear and generalized linear mixed effects models  Latent variable methods for ordinal data.
This book provides a compact selfcontained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement basic Bayesian data analyses using standard statistical models and to extend the standard models to specialized data analysis situations. The book begins with fundamental notions such as probability, exchangeability and Bayes' rule, and ends with modern topics such as variable selection in regression, generalized linear mixed effects models, and semiparametric copula estimation. Numerous examples from the social, biological and physical sciences show how to implement these methodologies in practice. Monte Carlo summaries of posterior distributions play an important role in Bayesian data analysis. The opensource R statistical computing environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statistical models and example Rcode is provided throughout the text. Much of the example code can be run ``as is'' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book. Peter Hoff is an Associate Professor of Statistics and Biostatistics at the University of Washington. He has developed a variety of Bayesian methods for multivariate data, including covariance and copula estimation, cluster analysis, mixture modeling and social network analysis. He is on the editorial board of the Annals of Applied Statistics.
There are no comments for this item.