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;Stimulation-based inference;MCMC Algorithm;Bayesian Studies;Posterior Distribution;Bayesian Prediction;Jag Software;Undergraduate Bayesian textbook;Posterior Predictive Distribution;probability distributions;MCMC Chain;regression models;Prior Distribution;Bayesian inference;Credible Interval;Metropolis and Gibbs sampling algorithms;Discrete Prior;Markov Chain and Monte Carlo algorithms;Simulated Draws;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;Stimulation-based inference;MCMC Algorithm;Bayesian Studies;Posterior Distribution;Bayesian Prediction;Jag Software;Undergraduate Bayesian textbook;Posterior Predictive Distribution;probability distributions;MCMC Chain;regression models;Prior Distribution;Bayesian inference;Credible Interval;Metropolis and Gibbs sampling algorithms;Discrete Prior;Markov Chain and Monte Carlo algorithms;Simulated Draws;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
