An Introductory Handbook of Bayesian Thinking
Autor Stephen C. Loftusen Limba Engleză Paperback – 2 aug 2024
- Utilizes real datasets to illustrate Bayesian models and their results
- Guides readers on coding Bayesian models using the statistical software R, including a helpful introduction and supporting online resource
- Appropriate for an undergraduate statistics course, as well as for non-statisticians with sufficient mathematical background (integral and differential Calculus and an introductory Statistics course)
- Covers any more advanced topics which readers may not be familiar with, such as the basic idea of vectors and matrices
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Specificații
ISBN-13: 9780323954594
ISBN-10: 0323954596
Pagini: 350
Dimensiuni: 152 x 229 x 15 mm
Greutate: 0.49 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0323954596
Pagini: 350
Dimensiuni: 152 x 229 x 15 mm
Greutate: 0.49 kg
Editura: ELSEVIER SCIENCE
Cuprins
1. Probability and Random Variables
2. Probability Distributions, Expected Value, and Variance
3. Common Probability Distributions
4. Conditional Probability and Bayes' Rule
5. Finding and Using Distributions of Data
6. Marginal and Conditional Distributions
7. The Bayesian Switch
8. A Brief Review of R
9. Single Parameter Bayesian Inference
10. Multi-Parameter Inference
11. Gibbs Sampling in R
12. Bayesian Linear Regression
13. Bayesian Binary Regression
14. Probabilistic Clustering
15. Dealing with Non-conjugate Priors
16. Models for Count Data
17. Testing Hypotheses with Bayes
18. Bayesian Inference Beyond This Book
Appendix A: Matrix Form of Bayesian Linear Regression
Appendix B: Multivariate Clustering
Appendix C: List of Probability Distributions
Appendix D: Solutions to Practice Problems
2. Probability Distributions, Expected Value, and Variance
3. Common Probability Distributions
4. Conditional Probability and Bayes' Rule
5. Finding and Using Distributions of Data
6. Marginal and Conditional Distributions
7. The Bayesian Switch
8. A Brief Review of R
9. Single Parameter Bayesian Inference
10. Multi-Parameter Inference
11. Gibbs Sampling in R
12. Bayesian Linear Regression
13. Bayesian Binary Regression
14. Probabilistic Clustering
15. Dealing with Non-conjugate Priors
16. Models for Count Data
17. Testing Hypotheses with Bayes
18. Bayesian Inference Beyond This Book
Appendix A: Matrix Form of Bayesian Linear Regression
Appendix B: Multivariate Clustering
Appendix C: List of Probability Distributions
Appendix D: Solutions to Practice Problems