We consider Bayesian information collection, in which a measurement policy collects information to support a future decision. This framework includes ranking and selection, continuous global optimization, and many other problems in sequential experimental design. We give a sufficient condition under which measurement policies sample each measurement type infinitely often, ensuring consistency, ...

Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper devel...

This paper investigates the reduction of variance associated with a simulation output performance measure, using the Sequential Sampling method while applying minimum simulation replications, for a class of JIT (Just in Time) warehousing system called crossdocking. We initially used the Sequential Sampling method to attain a desired 95% confidence interval half width of ± 0.5 for our chosen per...

A total of 24 commercial fields of cabbages and Brussels sprouts were sampled in a grid fashion with 20-25 equally spaced cells with four plants per cell. Using this data base of 80-100 plants, we conducted computer stimulations to compare the treatment decisions that would be made for the major insect pests using published sequential sampling programs and a newly developed variable-intensity s...

We describe a costand constraint-based decision-theoretic approach to the design of screening trials, where the goal is to identify promising candidates for future study. An algorithmic method for optimizing this approach is presented. This method utilizes a highly flexible structure to reflect a variety of decision and experimental costs and constraints. The designs produced can range from bei...

When observations are incomplete or data are missing, approximate inference methods based on importance sampling are often used. Unfortunately, when the target and proposal distributions are dissimilar, the sampling procedure leads to biased estimates or requires a prohibitive number of samples. Our method approximates a multivariate target distribution by sampling from an existing, sequential ...

The objective of this study is to develop a new modified Bayesian Kriging (MBKG) surrogate modeling method that can be used to carry out confidence-based reliability-based design optimization (RBDO) for problems in which simulation analyses are inherently noisy and standard Kriging approaches fail. The formulation of the MBKG surrogate modeling method is presented, and the full conditional dist...