Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
Introduction to Applied Bayesian Statistics and Estimation for Social Scientists covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models tha...
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| Format: | eBook |
| Language: | English |
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New York, NY
Springer New York
2007, 2007
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| Edition: | 1st ed. 2007 |
| Series: | Statistics for Social and Behavioral Sciences
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Probability Theory and Classical Statistics
- Basics of Bayesian Statistics
- Modern Model Estimation Part 1: Gibbs Sampling
- Modern Model Estimation Part 2: Metroplis–Hastings Sampling
- Evaluating Markov Chain Monte Carlo Algorithms and Model Fit
- The Linear Regression Model
- Generalized Linear Models
- to Hierarchical Models
- to Multivariate Regression Models
- Conclusion.