Title of dissertation: THE BAYESIAN AND APPROXIMATE BAYESIAN METHODS IN SMALL AREA ESTIMATION Santanu Pramanik, Doctor of Philosophy, 2008 Dissertation directed by: Professor Partha Lahiri Joint Program in Survey Methodology For small area estimation, model based methods are preferred to the traditional design based methods because of their ability to borrow strength from related sources. The i...

Multi-agent systems are increasingly popular approach to control of complex industrial processes. The idea of distribution of a complex task into many semi-autonomous cooperating units has been formalized using many frameworks. In this paper, we review the close relation of distributed Bayesian decision making and multi-agent systems. The Bayesian methodology was primarily designed for systems ...

Purpose – To counteract the effects of global competition, many organizations have extended their enterprises by forming supply chain networks. However, as organizations increase their dependence on these networks, they become more vulnerable to their suppliers’ risk profiles. The purpose of this paper is to present a methodology for modeling and evaluating risk profiles in supply chains via Ba...

Francesco de Pasquale Dynamic Magnetic Resonance Imaging is a non-invasive technique that provides an image sequence based on dynamic information for locating lesions and investigating their structures. In this thesis we develop new methodology for analysing dynamic Magnetic Resonance image sequences of the breast. This methodology comprises an image restoration step that reduces random distort...

Bayesian approaches have been proposed by several functional magnetic resonance imaging (fMRI) researchers in order to overcome the fundamental limitations of the popular statistical parametric mapping method. However, the difficulties associated with subjective prior elicitation have prevented the widespread adoption of the Bayesian methodology by the neuroimaging community. In this paper, we ...

We introduce a methodology for perfect simulation using so called catalysts to modify random fields. Our methodology builds on a number of ideas previously introduced by Breyer and Roberts (1999), by Murdoch (1999), and by Wilson (1999). We illustrate our techniques by simulating two examples of Bayesian posterior distributions.

Multivariate ordinal data arise in many areas of applications. This paper proposes new efficient methodology for Bayesian inference for multivariate probit models using Markov chain Monte Carlo techniques. The key idea for our approach is the novel use of parameter expansion to sample correlation matrices. We also propose methodology for model selection. Our approach is demonstrated through sev...

This paper proposes a bayesian methodology to treat the who’s who problem arising in individual level data sets such as patent data. We assess the usefullness of this methodology on the set of all French inventors appearing on EPO applications from 1978 to 2003.

Raghuram, Sandeep Mudabail. M.S., Purdue University, August, 2010. Bridging Text Mining and Bayesian Networks. Major Professor: Yuni Xia. After the initial network is constructed using expert’s knowledge of the domain, Bayesian networks need to be updated as and when new data is observed. Literature mining is a very important source of this new data. In this work, we explore what kind of data n...

In the field of reconnaissance and in many other real world applications, information from different possibly heterogenous information sources has to be fused for obtaining adequate results. We present a local Bayesian approach which is realized via an agent based architecture. In analogy to criminalistic investigators, fusion agents elaborate the posterior Degree of Belief of initial hypothese...