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We consider optimization via simulation over a finite set of alternatives. We employ a Bayesian value-of-information approach in which we allow both correlated prior beliefs on the sampling means and correlated sampling. Correlation in the prior belief allow us to learn about an alternative's value from samples of similar alternatives. Correlation in sampling, achieved through common random numbers,...
Identifying agents in a linear mixture is a fundamental problem in spectral sensing applications including chemical and biological agent identification. In general, the size of the spectral signature library is usually much larger than the number of agents really present. Based on this fact, the sparsity of the mixing coefficient vector can be utilized to help improve the identification performance...
In this paper, a data-driven model based on Bayesian Networks (BNs) is presented for the risk analysis of software development. The modeling process consists of three phases: BN initialization, conflict analysis, and risk monitoring and analysis. Using new project data obtained from the process of software development, the model can continually estimate risk probability, identify the sources of risk,...
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