Introductory Handbook of Bayesian Thinking
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Introductory Handbook of Bayesian Thinking
Loftus, Stephen C.
Elsevier Science & Technology
08/2024
350
Mole
Inglês
9780323954594
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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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Statistics; Bayesian Statistics; Statistical Computing; Markov Chain Monte Carlo Samplers; Probability; Hierarchical Models; Gibbs Sampling
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
