rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: A
    title: First Course in Bayesian Statistical Methods
  name:
    name part: Hoff, Peter D.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2009
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 272 p. online resource.
  abstract: This book provides a compact self-contained 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 open-source R statistical computing environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statistical models and example R-code 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.
  table of contents: and examples -- Belief, probability and exchangeability -- One-parameter 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 Metropolis-Hastings algorithms -- Linear and generalized linear mixed effects models -- Latent variable methods for ordinal data.
  note: by Peter D. Hoff.
  subject:
    topic: Mathematics
  subject:
    topic: Mathematical statistics
  subject:
    topic: Operations research
  subject:
    topic: Management science
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Econometrics
  subject:
    topic: Social sciences
  subject:
    topic: Mathematics
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Operations Research, Management Science
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Methodology of the Social Sciences
  subject:
    topic: Probability and Statistics in Computer Science
  subject:
    topic: Econometrics
  classification: QA273.A1-274.9
  classification: QA274-274.9
  classification: 519.2
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Texts in Statistics
  identifier: 9780387924076
  identifier: http://dx.doi.org/10.1007/978-0-387-92407-6
  record info:
    record creation date: 100301
    record change date: 20151204154308.0
    record identifier: 978-0-387-92407-6