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 for Animal Scientists
    sub title: The Basics
  name:
    name part: Blasco, Agustín.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 275 p. 62 illus., 57 illus. in color. online resource.
  abstract: In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference.
  table of contents: Foreword -- Notation -- 1. Do we understand classical statistics? -- 2. The Bayesian choice -- 3. Posterior distributions -- 4. MCMC -- 5. The “baby” model -- 6. The linear model. I. The “fixed” effects model -- 7. The linear model. II. The “mixed” model -- 8. A scope of the possibilities of Bayesian inference + MCMC -- 9. Prior information -- 10. Model choice -- Appendix -- References.
  note: by Agustín Blasco.
  subject:
    topic: Life sciences
  subject:
    topic: Agriculture
  subject:
    topic: Biostatistics
  subject:
    topic: Animal genetics
  subject:
    topic: Biomathematics
  subject:
    topic: Life Sciences
  subject:
    topic: Agriculture
  subject:
    topic: Veterinary Medicine/Veterinary Science
  subject:
    topic: Mathematical and Computational Biology
  subject:
    topic: Animal Genetics and Genomics
  subject:
    topic: Biostatistics
  classification: S1-S972
  classification: 630
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319542744
  identifier: http://dx.doi.org/10.1007/978-3-319-54274-4
  record info:
    record creation date: 170830
    record change date: 20170830135753.0
    record identifier: 978-3-319-54274-4