UAI 9th Bayesian Modeling Applications Workshop
Call for Papers
Saturday, August 18th, 2012
Catalina Island, California, USA.

www.abnms.org/uai2012-apps-workshop

Special theme: Temporal Modeling

The 9th Bayesian Modeling Applications Workshop solicits submissions of real-world applications of graphical models and Bayesian networks,in particular those dealing with temporal modeling. Our desire is to foster discussion and interchange about novel contributions that can speak to both the academic and the larger research community. Accordingly, we seek submissions also from practitioners and tool developers as well as researchers.

Bayesian networks are now a powerful, well-established technology for reasoning under uncertainty, supported by a wide range of mature academic and commercial software tools. They are now being applied in many domains, including environmental and ecological modeling, bioinformatics, medical decision support, many types of engineering, robotics, military, financial and economic modeling, education, forensics, emergency response, surveillance, and so on. We welcome submissions describing such real world applications, whether as stand-alone BNs or where the BNs are embedded in a larger software system. We encourage authors to address the practical issues involved in developing real-world applications, such as knowledge engineering methodologies, elicitation techniques, defining and meeting client needs, validation processes and integration methods, as well as software tools to these support these activities.

We particularly encourage the submission of papers that address the workshop theme of temporal modeling. Recently communities building dynamic Bayes networks (DBNs) and partially observable MDPs (POMDPs) are coming to realize that they are applying their methods to identical applications. Similarly POMDPs and other probabilistic methods are now established in the field of Automated Planning. Stochastic process models such as continuous time Bayes networks (CTBNs) should also be considered as part of this trend. Adaptive and on-line learning models also fit into this focus.