NOMBAS: a Bayesian procedure for selecting the greatest mean
Washburn, Alan R.
MetadataShow full item record
NOMBAS is an acronym for NOrmal Myopic Bayes Sequential, and is the name of a Bayesian procedure for selecting the category with the greatest mean. This paper describes NOMBAS in detail and then compares it with other procedures on the basis of Bayes risk versus average sample number.
NPS Report NumberNPS55-82-017
Showing items related by title, author, creator and subject.
A decision aid model for a maneuver force commander that incorporates the quantified judgment model Moughon, James Coleman (Monterey, California. Naval Postgraduate School, 1989-03);The commander on the modern battlefield has the responsibility of supervising more assets and evaluating more information than ever before. Therefore, there exists a need for an aid to assist the commander in selecting a ...
Seo, Young Uk. (Monterey, California : Naval Postgraduate School, 1988);Cost and Effectiveness models are developed for selecting a new tactical communication system in the Korean Army. Alternatives included an off-the-shelf purchase of existing U.S technology and four variants of a self-developed ...
Simulation screening experiments using lasso-optimal supersaturated design and analysis: a maritime operations application Xing, Dadi; Wan, Hong; Zhu, Michael Yu; Sanchez, Susan M.; Kaymal, Turgut (2013);Screening methods are beneficial for studies involving simulations that have a large number of variables where a relatively small (but unknown) subset is important. In this paper, we show how a newly proposed Lasso-optimal ...