A survey of high school students' opinions in regard to factors important in moral development and conduct

Not available.Hubert Chipman McHargueNot ListedNot ListedMaster of ScienceDepartment Not ListedCunningham Memorial Library, Terre Haute, Indiana State University.uisua-thesis-1948-mchargueMastersTitle from document title page. Document formatted into pages: contains 36p. : ill. Includes bibliography

The phase 2 NRA

We present points of special interest to potential proposers for the Compton Observatory Phase 2 Guest Investigator (GI) program. A general summary of some of the most important details of the phase 2 NASA Research Announcement (NRA) is followed by an enumeration of the modes of participation and proposal types available to GI proposers. Finally, the method which is planned for the selection of the Phase 2 Guest Investigators in parallel with the development of a preliminary Phase 2 observing timeline is outlined. The ways in which the selection of targets by GI's could be affected by the Phase 2 timeline development procedure is described

Sequential Design for Computer Experiments with a Flexible Bayesian Additive Model

In computer experiments, a mathematical model implemented on a computer is used to represent complex physical phenomena. These models, known as computer simulators, enable experimental study of a virtual representation of the complex phenomena. Simulators can be thought of as complex functions that take many inputs and provide an output. Often these simulators are themselves expensive to compute, and may be approximated by "surrogate models" such as statistical regression models. In this paper we consider a new kind of surrogate model, a Bayesian ensemble of trees (Chipman et al. 2010), with the specific goal of learning enough about the simulator that a particular feature of the simulator can be estimated. We focus on identifying the simulator's global minimum. Utilizing the Bayesian version of the Expected Improvement criterion (Jones et al. 1998), we show that this ensemble is particularly effective when the simulator is ill-behaved, exhibiting nonstationarity or abrupt changes in the response. A number of illustrations of the approach are given, including a tidal power application.Comment: 21 page