- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.
In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics:
Advice on selecting prior distributions
Computational methods including Markov chain Monte Carlo (MCMC)
Model-comparison and goodness-of-fit measures, including sensitivity to priors
Frequentist properties of Bayesian methods
Case studies covering advanced topics illustrate the flexibility of the Bayesian approach:
Semiparametric regression
Handling of missing data using predictive distributions
Priors for high-dimensional regression models
Computational techniques for large datasets
Spatial data analysis
The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book's website.
Contents
1. Introduction to Bayesian Inferential Framework. 2. Prior Knowledge to Posterior Inference. 3. Computational Methods. 4. Linear and Generalized Linear Regression Methods. 5. Models for Large Dimensional Parameters. 6. Models for Dependent Data. 7. Models for Data with Irregularities. 8. Models for Infinite Dimensional Parameters. 9. Advanced Computational Methods. 10. Case Studies Using Advanced Bayesian Methods
The code and data is at https://bayessm.wordpress.ncsu.edu/.
