STAT8810 (Fall, 2017)

Special Topics in Uncertainty Quantification via Tree-based Models and Approximate Computations



Sacks, Welch, Mitchell and Wynn: Design and Analysis of Computer Experiments
Santner, Williams and Notz: The Design and Analysis of Computer Experiments
Cressie: Statistics for Spatial Data
Bevilaqua, Faouzi, Furrer and Porcu: Estimation and Prediction using Generalized Wendland Covariance Functions under Fixed Domain Asymptotics
Bottou: Large-Scale Machine Learning with Stochastic Gradient Descent
McKay, Beckman and Conover: Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
Jones, Schonlau and Welch: Efficient Global Optimization of Expensive Black Box Functions
Oakley: Eliciting Gaussian Process Priors for Complex Computer Codes
Chipman, George and McCulloch: Bayesian CART Model Search
Chipman, George and McCulloch: BART: Bayesian Additive Regression Trees 
Pratola: Efficient Metropolis–Hastings Proposal Mechanisms for Bayesian Regression Tree Models
Gramacy and Lee: Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
Cowles and Carlin: MCMC Convergence Diagnostics: A Comparative Review
Geyer: Practical MCMC
Raftery and Lewis: How Many Iterations in the Gibbs Sampler?
Geweke: Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments
Gelman and Rubin: Inference from Iterative Simulation Using Multiple Sequences
Kennedy and O’Hagan: Bayesian Calibration of Computer Models


Assignment 1
Assignment 2 (frankesinputs.dat)
Assignment 3 (co2plume.dat, co2holdout.dat)

Project outline is here.