Bayesian Statistics for Beginners
-10%
portes grátis
Bayesian Statistics for Beginners
a step-by-step approach
Donovan, Therese M.; Mickey, Ruth M.
Oxford University Press
05/2019
432
Mole
Inglês
9780198841302
15 a 20 dias
882
Descrição não disponível.
Section 1
Basics of Probability
1: Introduction to Probability
2: Joint, Marginal, and Conditional Probability
Section 2
Bayes' Theorem and Bayesian Inference
3: Bayes' Theorem
4: Bayesian Inference
5: The Author Problem - Bayesian Inference with Two Hypotheses
6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses
7: The Portrait Problem: Bayesian Inference with Joint Likelihood
Section 3
Probability Functions
8: Probability Mass Functions
9: Probability Density Functions
Section 4
Bayesian Conjugates
10: The White House Problem: The Beta-Binomial Conjugate
11: The Shark Attack Problem: The Gamma-Poisson Conjugate
12: The Maple Syrup Problem: The Normal-Normal Conjugate
Section 5
Markov Chain Monte Carlo
13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm
14: MCMC Diagnostic Approaches
15: The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm
16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling
Section 6
Applications
17: The Survivor Problem: Simple Linear Regression with MCMC
18: The Survivor Problem Continued: Introduction to Bayesian Model Selection
19: The Lorax Problem: Introduction to Bayesian Networks
20: The Once-ler Problem: Introduction to Decision Trees
Appendices
Appendix 1: The Beta-Binomial Conjugate Solution
Appendix 2: The Gamma-Poisson Conjugate Solution
Appendix 3: The Normal-Normal Conjugate Solution
Appendix 4: Conjugate Solutions for Simple Linear Regression
Appendix 5: The Standardization of Regression Data
Basics of Probability
1: Introduction to Probability
2: Joint, Marginal, and Conditional Probability
Section 2
Bayes' Theorem and Bayesian Inference
3: Bayes' Theorem
4: Bayesian Inference
5: The Author Problem - Bayesian Inference with Two Hypotheses
6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses
7: The Portrait Problem: Bayesian Inference with Joint Likelihood
Section 3
Probability Functions
8: Probability Mass Functions
9: Probability Density Functions
Section 4
Bayesian Conjugates
10: The White House Problem: The Beta-Binomial Conjugate
11: The Shark Attack Problem: The Gamma-Poisson Conjugate
12: The Maple Syrup Problem: The Normal-Normal Conjugate
Section 5
Markov Chain Monte Carlo
13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm
14: MCMC Diagnostic Approaches
15: The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm
16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling
Section 6
Applications
17: The Survivor Problem: Simple Linear Regression with MCMC
18: The Survivor Problem Continued: Introduction to Bayesian Model Selection
19: The Lorax Problem: Introduction to Bayesian Networks
20: The Once-ler Problem: Introduction to Decision Trees
Appendices
Appendix 1: The Beta-Binomial Conjugate Solution
Appendix 2: The Gamma-Poisson Conjugate Solution
Appendix 3: The Normal-Normal Conjugate Solution
Appendix 4: Conjugate Solutions for Simple Linear Regression
Appendix 5: The Standardization of Regression Data
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Section 1
Basics of Probability
1: Introduction to Probability
2: Joint, Marginal, and Conditional Probability
Section 2
Bayes' Theorem and Bayesian Inference
3: Bayes' Theorem
4: Bayesian Inference
5: The Author Problem - Bayesian Inference with Two Hypotheses
6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses
7: The Portrait Problem: Bayesian Inference with Joint Likelihood
Section 3
Probability Functions
8: Probability Mass Functions
9: Probability Density Functions
Section 4
Bayesian Conjugates
10: The White House Problem: The Beta-Binomial Conjugate
11: The Shark Attack Problem: The Gamma-Poisson Conjugate
12: The Maple Syrup Problem: The Normal-Normal Conjugate
Section 5
Markov Chain Monte Carlo
13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm
14: MCMC Diagnostic Approaches
15: The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm
16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling
Section 6
Applications
17: The Survivor Problem: Simple Linear Regression with MCMC
18: The Survivor Problem Continued: Introduction to Bayesian Model Selection
19: The Lorax Problem: Introduction to Bayesian Networks
20: The Once-ler Problem: Introduction to Decision Trees
Appendices
Appendix 1: The Beta-Binomial Conjugate Solution
Appendix 2: The Gamma-Poisson Conjugate Solution
Appendix 3: The Normal-Normal Conjugate Solution
Appendix 4: Conjugate Solutions for Simple Linear Regression
Appendix 5: The Standardization of Regression Data
Basics of Probability
1: Introduction to Probability
2: Joint, Marginal, and Conditional Probability
Section 2
Bayes' Theorem and Bayesian Inference
3: Bayes' Theorem
4: Bayesian Inference
5: The Author Problem - Bayesian Inference with Two Hypotheses
6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses
7: The Portrait Problem: Bayesian Inference with Joint Likelihood
Section 3
Probability Functions
8: Probability Mass Functions
9: Probability Density Functions
Section 4
Bayesian Conjugates
10: The White House Problem: The Beta-Binomial Conjugate
11: The Shark Attack Problem: The Gamma-Poisson Conjugate
12: The Maple Syrup Problem: The Normal-Normal Conjugate
Section 5
Markov Chain Monte Carlo
13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm
14: MCMC Diagnostic Approaches
15: The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm
16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling
Section 6
Applications
17: The Survivor Problem: Simple Linear Regression with MCMC
18: The Survivor Problem Continued: Introduction to Bayesian Model Selection
19: The Lorax Problem: Introduction to Bayesian Networks
20: The Once-ler Problem: Introduction to Decision Trees
Appendices
Appendix 1: The Beta-Binomial Conjugate Solution
Appendix 2: The Gamma-Poisson Conjugate Solution
Appendix 3: The Normal-Normal Conjugate Solution
Appendix 4: Conjugate Solutions for Simple Linear Regression
Appendix 5: The Standardization of Regression Data
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
