Bayesian Data AnalysisWinner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied |
Contents
| 1 | |
Fundamentals of Bayesian Data Analysis | 139 |
Advanced Computation | 259 |
Regression Models | 351 |
Nonlinear and Nonparametric Models | 469 |
Appendixes | 575 |
References | 609 |
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Common terms and phrases
ˆθ applied approach assume average basis functions Bayesian analysis Bayesian inference binomial Chapter clusters coefficients computation conditional posterior distribution conjugate prior consider convergence corresponding covariance data points dataset Dirichlet Dirichlet process discuss effect estimands example expected factor Figure finite Gaussian process Gelman Gibbs sampler given hierarchical model histogram hyperparameters independent interval iterations joint posterior distribution likelihood linear model linear regression logistic marginal likelihood matrix measurements methods missing data mixture model model checking noninformative prior distribution normal approximation normal distribution normal model notation observed data obtained outcomes Poisson population posterior distribution posterior inferences posterior mean posterior mode posterior predictive distribution posterior probability posterior simulations predictors prior density prior distribution problem radon regression model sample scale Section simulation draws standard deviation statistical step survey treatment uniform prior distribution unknown updating values variance parameters variational Bayes vector yobs zero