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 Nonparametrics
  name:
    name part: Ghosh, J. K.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Ramamoorthi, R. V.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2003
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 308 p. online resource.
  abstract: Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonparametric methods of classical statistics. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian nonparametrics. Though the emphasis of the book is on nonparametrics, there is a substantial chapter on asymptotics of classical Bayesian parametric models. Jayanta Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently professor of statistics at Purdue University. He has been editor of Sankhya and served on the editorial boards of several journals including the Annals of Statistics. Apart from Bayesian analysis, his interests include asymptotics, stochastic modeling, high dimensional model selection, reliability and survival analysis and bioinformatics. R.V. Ramamoorthi is professor at the Department of Statistics and Probability at Michigan State University. He has published papers in the areas of sufficiency invariance, comparison of experiments, nonparametric survival analysis and Bayesian analysis. In addition to Bayesian nonparametrics, he is currently interested in Bayesian networks and graphical models. He is on the editorial board of Sankhya.
  table of contents: Introduction: Why Bayesian Nonparametrics—An Overview and Summary -- Preliminaries and the Finite Dimensional Case -- M(?) and Priors on M(?) -- Dirichlet and Polya tree process -- Consistency Theorems -- Density Estimation -- Inference for Location Parameter -- Regression Problems -- Uniform Distribution on Infinite-Dimensional Spaces -- Survival Analysis—Dirichlet Priors -- Neutral to the Right Priors -- Exercises.
  note: by J. K. Ghosh, R. V. Ramamoorthi.
  subject:
    topic: Statistics
  subject:
    topic: Statistics
  subject:
    topic: Statistical Theory and Methods
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Series in Statistics
  identifier: 9780387226545
  identifier: http://dx.doi.org/10.1007/b97842
  record info:
    record creation date: 100301
    record change date: 20151204162244.0
    record identifier: 978-0-387-22654-5