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:
    title: Bayesian Data Analysis, Third Edition, 3rd Edition
  name:
    name part: Gelman, Andrew
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Carlin, John
    role:
      role term: author.
  name:
    name part: Stern, Hal
    role:
      role term: author.
  name:
    name part: Dunson, David
    role:
      role term: author.
  name:
    name part: Vehtari, Aki
    role:
      role term: author.
  name:
    name part: Rubin, Donald
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    date issued: 2013
    edition: 3rd edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (675 pages)
  abstract: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors-all leaders in the statistics community-introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book's web page.
  note: Gelman, Andrew.
  note: Electronic resource.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2013.
  note: Mode of access: World Wide Web.
  identifier: 9781439898222
  identifier: 9781439898208
  identifier: 9781439840962
  identifier: 1439840954
  identifier: 1439898200
  identifier: 1439840962
  identifier: 9781439840955
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 270417
    record change date: 20260130162457.2
    record identifier: in00004142539