Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
International Journal of Intelligent Systems - Uncertain Reasoning (Part 1)
A Bayesian framework for groundwater quality assessment
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Dynamic Bayesian networks for audio-visual speech recognition
EURASIP Journal on Applied Signal Processing
When do numbers really matter?
Journal of Artificial Intelligence Research
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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The emphasis on the need to protect groundwater quality has resulted in an increased interest in groundwater quality assessment. Water experts and researchers in the area have been, however, arguing that the currently used techniques are not accurate means of measuring groundwater contamination. It is mainly because these techniques neglect not only the probabilistic dependencies between pollutants but also the precision and the accuracy of the tested methods used by environmental laboratories. Therefore, this work describes the development and application of a prototype Dynamic Bayesian Network (DBN) that addresses these problems through the use of a temporal probabilistic model. First, we present a new technique for data preprocessing. Then we describe the network models we developed, as well as the methods used to build these models. Various challenges, such as acquiring groundwater datasets, identifying pollutants and anticipating potential problem contaminants, are addressed. Finally, we present the results of applications of these models.