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: Likelihood, Bayesian and MCMC methods in quantitative genetics
  name:
    name part: Sorensen, Daniel.
    role:
      role term: creator
  name:
    name part: Gianola, Daniel
    name part: 1947-
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: nyu
    date issued: 2002
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xvii, 740 pages) : illustrations.
  abstract: Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as mathematicians, probabilists, statisticians, computer scientists and statistical geneticists. Specifically, the MCMC "revolution" has made a deep impact in quantitative genetics. This can be seen, for example, in the vast number of papers dealing with complex hierarchical models and models for detection of genes affecting quantitative or meristic traits in plants, animals and humans that have been published recently. This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style, and have been written by and addressed to professional statisticians. For this reason, considerable more detail is offered than what may be warranted for a more mathematically apt audience. The book is divided into four parts. Part I gives a review of probability and distribution theory. Parts II and III present methods of inference and MCMC methods. Part IV discusses several models that can be applied in quantitative genetics, primarily from a bayesian perspective. An effort has been m…
  table of contents: Cover -- Preface -- Table of Contents -- 1. Uncertainty, Random Variables, and Probability Distributions -- 2. Uncertainty about Functions of Random Variables -- 3. An Introduction to Likelihood Inference -- 4. Further Topics in Likelihood Inference -- 5. An Introduction to Bayesian Inference -- 6. Bayesian Analysis of Linear Models -- 7. The Prior Distribution and Bayesian Analysis -- 8. Bayesian Assessment of Hypotheses and Models -- 9. Approximate Inference Via the EM Algorithm -- 10. An Overview of Discrete Markov Chains -- 11. Markov Chain Monte Carlo -- 12. Implementation and Analysis of MCMC Samples -- 13. Gaussian and Thick-Tailed Linear Models -- 14. Threshold Models for Categorical Responses -- 15. Bayesian Analysis of Longitudinal Data -- 16. Introduction to Segregation and Quantitative Trait Loci Analysis -- References -- List of Citations.
  note: Daniel Sorensen, Daniel Gianola.
  note: Electronic resource.
  note: Includes bibliographical references (pages [701]-726) and index.
  subject:
    topic: Genetics
    topic: Statistical methods
  subject:
    topic: Monte Carlo method
  subject:
    topic: Markov processes
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: SCIENCE
    topic: Life Sciences
    topic: Evolution
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: Genetics
    topic: Statistical methods
  subject:
    topic: Markov processes
  subject:
    topic: Monte Carlo method
  subject:
    topic: Electronic books
  subject:
    topic: Biometry
    topic: methods
  subject:
    topic: Genetics, Medical
    topic: methods
  subject:
    topic: Monte Carlo Method
  subject:
    topic: Probability
  classification: QH438.4.S73 S675 2002eb
  classification: 575.6/07/27
  classification: 2003 G-689
  classification: WA 950 S713L 2002
  related item:
    title info:
      title: Likelihood, Bayesian and MCMC methods in quantitative genetics
    name:
      name part: Sorensen, Daniel.
    origin info:
      publisher: New York : Springer-Verlag, ©2002
    identifier: (DLC) 2002019555
    identifier: (OCoLC)48907138
  related item:
    title info:
      title: Statistics for biology and health
  identifier: 0387227644 (electronic bk.)
  identifier: 9780387227641 (electronic bk.)
  identifier: 9780387954400
  identifier: 0387954406
  identifier: https://proxy.library.tamu.edu/login
  related item:
    location:
      url: https://proxy.library.tamu.edu/login
  record info:
    record content source: N$T
    record creation date: 031008
    record change date: 20220105141232.0
    record identifier: in00001847828
    language of cataloging:
      language term: eng