In this paper, we focus on inferring social roles in conversations using information extracted only from the speaking styles of the speakers. We model the turn-taking behavior of the speakers with dynamic Bayesian networks (DBNs), which provide the capability of naturally formulating the dependencies between random variables. More specifically, we first explore the usefulness of a simple DBN, n...

composting as one of the municipal solid waste management strategies aims to reduce size and weight of excreted substances, to abate odor and leachate, increase resource recovery and reduce the cost of disposal. environmental impact assessment (eia) of compost plants is required for compliance with laws and regulations. eia is one of the effective methods to protect environment. the aim of this...

Incorporating prior knowledge into black-box classifiers is still much of an open problem. We propose a hybrid Bayesian methodology that consists in encoding prior knowledge in the form of a (Bayesian) belief network and then using this knowledge to estimate an informative prior for a black-box model (e.g. a multilayer perceptron). Two technical approaches are proposed for the transformation of...

PANSOMBUT, TATDOW. Advanced Learning Techniques for Improved Inference of Bayesian Belief Networks from Uncertain and High-dimensional Data. (Under the direction of Prof. Nagiza F. Samatova and Prof. Dennis R. Bahler.) A Bayesian Belief Network (BBN) is a powerful probabilistic learning model, it has been used successfully in many problem domains, such as medical diagnostics, computational biol...

This paper presents a novel approach to simulation metamodeling using dynamic Bayesian networks (DBNs) in the context of discrete event simulation. A DBN is a probabilistic model that represents the joint distribution of a sequence of random variables and enables the efficient calculation of their marginal and conditional distributions. In this paper, the construction of a DBN based on simulati...