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Likelihood, Bayesian and MCMC methods in quantitative genetics /

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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 applica...

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Bibliographic Details
Main Author: Sorensen, Daniel
Corporate Author: SpringerLink (Online service)
Other Authors: Gianola, Daniel, 1947-
Format: eBook
Language:English
Published: New York : Springer-Verlag, [2002]
Series:Statistics for biology and health.
Subjects:
Genetics > Statistical methods.
Monte Carlo method.
Markov processes.
Bayesian statistical decision theory.
SCIENCE > Life Sciences > Evolution.
Electronic books.
Biometry > methods.
Genetics, Medical > methods.
Monte Carlo Method.
Probability.
Online Access:Connect to the full text of this electronic book
Connect to the full text of this electronic book
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.