In the past decade, there have been enormous advances in the use of Bayesian methodology for analysis of epidemiologic data, and there are now many practical advantages to the Bayesian approach. Bayesian models can easily accommodate unobserved variables such as an individual's true disease status in the presence of diagnostic error. The use of prior probability distributions represents a power...

Research and Education Activities: a) Bayesian Model Synthesis (Steven MacEachern and Mario Peruggia, with student Qingzhao Yu) We address the problem of how to conduct a legitimate Bayesian analysis while still allowing one to develop likelihood and prior by exploring the data. The methodology is based on a principle of data splitting, whereby several analysts examine portions of a data set, w...

This paper describes a general Bayesian methodology for reservoir operations. This methodology is applied to the operation of the two main reservoirs in the Zambezi river: Lake Kariba and Cahora Bassa.

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor ...

In acceptance sampling plans, the decisions on either accepting or rejecting a specific batch is still a challenging problem. In order to provide a desired level of protection for customers as well as manufacturers, in this paper, a new acceptance sampling design is proposed to accept or reject a batch based on Bayesian modeling to update the distribution function of the percentage of nonconfor...

A bayesian network is an appropriate tool for working with uncertainty and probability, that are typical of real-life applications. In literature we find different approaches for bayesian network learning. Some of them are based on search and score methodology and the others follow an information theory based approach. One of the most known algorithm for learning bayesian network is the SLA alg...