Probability and Bayesian Modeling
-10%
portes grátis
Probability and Bayesian Modeling
Albert, Jim; Hu, Jingchen
Taylor & Francis Ltd
12/2019
552
Dura
Inglês
9781138492561
15 a 20 dias
1093
Descrição não disponível.
1. Introduction, examples and review. 2. Why Bayes? 3. One-parameter models. 4. Monte Carlo approximation. 5. Normal models. 6. Gibbs sampler. 7. Metropolis-Hastings algorithms, BUGS. 8. Bayesian hierarchical modeling. 9. Multivariate normal models. 10. Bayesian linear regression. 11. Bayesian model comparison, variable selection and model selection. 12. Applications.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
MCMC Output;MCMC Algorithm;Stimulation-based inference;Posterior Distribution;Bayesian Studies;Jag Software;Bayesian Prediction;Posterior Predictive Distribution;Undergraduate Bayesian textbook;MCMC Chain;probability distributions;Prior Distribution;regression models;Credible Interval;Bayesian inference;Discrete Prior;Metropolis and Gibbs sampling algorithms;Simulated Draws;Markov Chain and Monte Carlo algorithms;Posterior Density;Bayesian Credible Intervals;Regression Model;Posterior Predictive;Interval Estimate;Home Run Rates;Gibbs Sampling;Joint Probability Mass Function;Bivariate Normal;Latent Class Model;MCMC Step;Implement Gibbs Sampling;Negative Binomial Sampling;Binomial Experiment;Log Income
1. Introduction, examples and review. 2. Why Bayes? 3. One-parameter models. 4. Monte Carlo approximation. 5. Normal models. 6. Gibbs sampler. 7. Metropolis-Hastings algorithms, BUGS. 8. Bayesian hierarchical modeling. 9. Multivariate normal models. 10. Bayesian linear regression. 11. Bayesian model comparison, variable selection and model selection. 12. Applications.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
MCMC Output;MCMC Algorithm;Stimulation-based inference;Posterior Distribution;Bayesian Studies;Jag Software;Bayesian Prediction;Posterior Predictive Distribution;Undergraduate Bayesian textbook;MCMC Chain;probability distributions;Prior Distribution;regression models;Credible Interval;Bayesian inference;Discrete Prior;Metropolis and Gibbs sampling algorithms;Simulated Draws;Markov Chain and Monte Carlo algorithms;Posterior Density;Bayesian Credible Intervals;Regression Model;Posterior Predictive;Interval Estimate;Home Run Rates;Gibbs Sampling;Joint Probability Mass Function;Bivariate Normal;Latent Class Model;MCMC Step;Implement Gibbs Sampling;Negative Binomial Sampling;Binomial Experiment;Log Income
