Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics
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...
| Main Authors: | , |
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| Corporate Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY :
Springer New York : Imprint: Springer,
2002.
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| Series: | Statistics for Biology and Health,
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| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Review of Probability and Distribution Theory
- Uncertainty, Random Variables, and Probability Distributions
- Uncertainty about Functions of Random Variables
- Methods of Inference
- An Introduction to Likelihood Inference
- Further Topics in Likelihood Inference
- An Introduction to Bayesian Inference
- Bayesian Analysis of Linear Models
- The Prior Distribution and Bayesian Analysis
- Bayesian Assessment of Hypotheses and Models
- Approximate Inference Via the EM Algorithm
- Markov Chain Monte Carlo Methods
- An Overview of Discrete Markov Chains
- Markov Chain Monte Carlo
- Implementation and Analysis of MCMC Samples
- Applications in Quantitative Genetics
- Gaussian and Thick-Tailed Linear Models
- Threshold Models for Categorical Responses
- Bayesian Analysis of Longitudinal Data
- to Segregation and Quantitative Trait Loci Analysis.
