Bayesian Data Analysis
- A. Gelman, J. Carlin, H. Stern, D. Dunson, Aki Vehtari, D. Rubin
- Mathematics, Computer Science
- 1 September 1996
Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Bayesian data analysis, third edition
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
An efficient computation of LOO is introduced using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights, and it is demonstrated that PSIS-LOO is more robust in the finite case with weak priors or influential observations.
Understanding predictive information criteria for Bayesian models
The Akaike, deviance, and Watanabe-Akaike information criteria are reviewed from a Bayesian perspective and it is better understood, through small examples, how these methods can apply in practice.
One vs three years of adjuvant imatinib for operable gastrointestinal stromal tumor: a randomized trial.
Adjuvant imatinib administered for 12 months after surgery has improved recurrence-free survival (RFS) of patients with operable gastrointestinal stromal tumor (GIST) compared with placebo and overall survival of GIST patients with a high risk of Gist recurrence.
Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts.
Rao-Blackwellized particle filter for multiple target tracking
Sparsity information and regularization in the horseshoe and other shrinkage priors
A concept of effective number of nonzero parameters is introduced, an intuitive way of formulating the prior for the global hyperparameter based on the sparsity assumptions is shown, and the previous default choices are argued to be dubious based on their tendency to favor solutions with more unshrunk parameters than the authors typically expect a priori.
Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion)
- Aki Vehtari, A. Gelman, Daniel P. Simpson, B. Carpenter, Paul-Christian Burkner
- Computer Science, Mathematics
- 19 March 2019
A collection of quantile-based local efficiency measures, along with a practical approach for computing Monte Carlo error estimates for quantiles, are introduced and it is suggested that common trace plots should be replaced with rank plots from multiple chains.
Gaussian processes with monotonicity information
Behaviour of the proposed method is illustrated with artificial regression examples, and the method is used in a real world health care classification problem to include monotonicity information with respect to one of the covariates.