A Bayesian Decision Theoretical Approach to Supervised Learning, Selective Sampling, and Empirical Function Optimization James L. Carroll Department of Computer Science Doctor of Philosophy Many have used the principles of statistics and Bayesian decision theory to model specific learning problems. It is less common to see models of the processes of learning in general. One exception is the mod...

The topic of this article is stochastic algorithms for evaluation of the utility and subjective probability based on the decision maker’s preferences. The main direction of the presentation is toward development of mathematically grounded algorithms for subjective probability and expected utility evaluation as a function of both the probability and the rank of the alternative. The stochastic as...

We develop a principled strategy to sample a function optimally for function approximation tasks within a Bayesian framework. Using ideas from optimal experiment design, we introduce an objective function (incorporating both bias and variance) to measure the degree of approximation, and the potential utility of the data points towards optimizing this objective. We show how the general strategy ...

to use services transparently in a distributed environment, the utility grids develop a cyber-infrastructure. the parameters of the quality of service such as the allocation-cost and makespan have to be dealt with in order to schedule workflow application tasks in the utility grids. optimization of both target parameters above is a challenge in a distributed environment and may conflict one ano...

It is difficult to understand a scene from visual information in uncertain real world. Since Bayesian network (BN) is known as good in this uncertainty, it has received significant attention in the area of vision-based scene understanding. However, BN-based modeling methods still have the difficulties in modeling complex relationships and combining several modules, as well as the high computati...

The theories of supermodular optimization and games provide a framework for the analysis of systems marked by complementarity. We summarize the principal results of these theories and indicate their usefulness by applying them to study the shift to 'modern manufacturing'. We also use them to analyze the characteristic features of the Lincoln Electric Company's strategy and structure.

The Weibull distribution has been widely used in survival and engineering reliability analysis. In life testing experiments is fairly common practice to terminate the experiment before all the items have failed, that means the data are censored. Thus, the main objective of this paper is to estimate the reliability function of the Weibull distribution with uncensored and censored data by using B...