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: An
    title: Introduction to Bayesian Analysis
    sub title: Theory and Methods
  name:
    name part: Ghosh, Jayanta K.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Delampady, Mohan.
    role:
      role term: author.
  name:
    name part: Samanta, Tapas.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2006
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 354 p. online resource.
  abstract: This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior. J.K. Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently a professor of statistics at Purdue University and professor emeritus at the Indian Statistical Institute. He has been the editor of Sankhya and has served on the editorial boards of several journals including the Annals of Statistics. His current interests in Bayesian analysis include asymptotics, nonparametric methods, high-dimensional model selection, reliability and survival analysis, bioinformatics, astrostatistics and sparse and …
  table of contents: Statistical Preliminaries -- Bayesian Inference and Decision Theory -- Utility, Prior, and Bayesian Robustness -- Large Sample Methods -- Choice of Priors for Low-dimensional Parameters -- Hypothesis Testing and Model Selection -- Bayesian Computations -- Some Common Problems in Inference -- High-dimensional Problems -- Some Applications.
  note: by Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta.
  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 Texts in Statistics
  identifier: 9780387354330
  identifier: http://dx.doi.org/10.1007/978-0-387-35433-0
  record info:
    record creation date: 100301
    record change date: 20151204180920.0
    record identifier: 978-0-387-35433-0