<\/p>\n
<\/p>\n
<\/p>\n
References Assignments
\nSacks, Welch, Mitchell and Wynn: Design and Analysis of Computer Experiments<\/a>
\nSantner, Williams and Notz: The Design and Analysis of Computer Experiments<\/a>
\nCressie: Statistics for Spatial Data<\/a>
\nBevilaqua, Faouzi, Furrer and Porcu: Estimation and Prediction using Generalized Wendland Covariance Functions under Fixed Domain Asymptotics<\/a>
\nBottou: Large-Scale Machine Learning with Stochastic Gradient Descent<\/a>
\nMcKay, Beckman and Conover: Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code<\/a>
\nJones, Schonlau and Welch: Efficient Global Optimization of Expensive Black Box Functions<\/a>
\nOakley: Eliciting Gaussian Process Priors for Complex Computer Codes<\/a>
\nChipman, George and McCulloch: Bayesian CART Model Search<\/a>
\nChipman, George and McCulloch: BART: Bayesian Additive Regression Trees<\/a>
\nPratola: Efficient Metropolis\u2013Hastings Proposal Mechanisms for Bayesian Regression Tree Models<\/a>
\nGramacy and Lee: Bayesian Treed Gaussian Process Models With an Application to Computer Modeling<\/a>
\nCowles and Carlin: MCMC Convergence Diagnostics: A Comparative Review<\/a>
\nGeyer: Practical MCMC<\/a>
\nRaftery and Lewis: How Many Iterations in the Gibbs Sampler?<\/a>
\nGeweke: Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments<\/a>
\nGelman and Rubin: Inference from Iterative Simulation Using Multiple Sequences<\/a>
\nKennedy and O’Hagan: Bayesian Calibration of Computer Models<\/a><\/p>\n
\nAssignment 1<\/a>
\nAssignment 2<\/a> (frankesinputs.dat<\/a>)
\nAssignment 3<\/a> (co2plume.dat<\/a>, co2holdout.dat<\/a>)<\/p>\n