Bayesian Data Analysis

  title={Bayesian Data Analysis},
  author={Andrew Gelman and John B. Carlin and Hal S. Stern and David B. Dunson and Aki Vehtari and Donald B. Rubin},
FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation… 

Bayesian model choice based on Monte Carlo estimates of posterior model probabilities

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

This work considers approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non‐Gaussian response variables and can directly compute very accurate approximations to the posterior marginals.

Non-linear regression models for Approximate Bayesian Computation

A machine-learning approach to the estimation of the posterior density by introducing two innovations that fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling.

Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models

Bayesian posterior mean estimates for Poisson hidden Markov models

Hierarchical estimation of parameters in Bayesian networks

Computational Bayesian Statistics

This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes, along with a brief but complete and mathematically rigorous introduction to Bayesian inference.

Accounting for parameter uncertainty in simulation input modeling

A Bayesian approach to probabilistic input modeling takes into account the parameter and stochastic uncertainties inherent in most simulations, and yields valid predictive inferences about the output quantities of interest.

Semiparametric Bayesian inference in multiple equation models

This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations