With the increase in environmental awareness, competitions and government policies, implementation of green supply chain management activities to sustain production and conserve resources is becoming more necessary for different organizations. However, it is difficult to successfully implement green supply chain (GSC) activities because of the risks involved. These risks alongside their resourc...

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...

Training deep belief networks (DBNs) is normally done with large data sets. Our goal is to predict traces of the surface of the tongue in ultrasound images of human speech. Hand-tracing is labor-intensive; the dataset is highly imbalanced since many images are extremely similar. We propose a bootstrapping method which handles this imbalance by iteratively selecting a small subset of images to b...

We investigate properties of Bayesian networks (BNs) in the context of state estimation. We introduce a coarse perspective on the inference processes and use this perspective to identify conditions under which state estimation with BNs can be very robust, even if the quality of the model is very low. By making plausible assumptions we can formulate asymptotic properties of the estimation perfor...

A current popular approach to representing time in Bayesian belief networks is through Dynamic Bayesian Networks (DBNs) (Dean & Kanazawa 1989). DBNs connect sequences of entire Bayes networks, each representing a situation at a snapshot in time. We present an alternative method for incorporating time into Bayesian belief networks that utilizes abstractions of temporal representation. This metho...

Finding the I Most Probable IJxplanations (MPE) of a given evidence, Se, in a Bayesian belief network is a process to identify and order a set of composite hypotheses, His, of which the posterior probabilities are the I largest; i.e., Pr(Hii&) 2 Pr(H21&) 2 . . . L Pr(&ISlz)~ A composite hypothesis is defined as an instantiation of all the non-evidence variables in the network. It could be shown...

In a teaching and learning environment Bayesian network fits well because it can adjust its structure as per data presented to it. When a Bayesian network learns with a huge number of data, its belief value is updated even if the change in belief is not significant. This causes a problem when the user’s preference changes over time. The learning process cannot catch up rapidly enough to handle ...

The basic frameworks and practical schemes of the Bayesian network and the belief propagation to the probabilistic image processing are reviewed. The probabilistic image processing is formulated by means of Bayesian statistics and Markov random fields. The system is regarded as one of Bayesian networks. In general, the Bayesian network has serious computational complexity because the probabilis...