While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and thei...
This paper investigates the problem of incorporating auxiliary information (e.g. pitch) for speech recognition using dynamic Bayesian networks (DBNs). Previous works usually model...
Bayesian inference methods are commonly applied to the classification of brain Magnetic Resonance images (MRI). We use the Maximum Evidence (ME) approach to estimate the most prob...
Recent work in applying causal modeling (Bayesian networks) to software engineering has resulted in improved decision support systems for software project managers. Once the causa...
Peter Hearty, Norman E. Fenton, Martin Neil, Patri...
In this paper, a competition-based connectionist model for diagnostic problem-solving is adapted to information retrieval. In this model, we treat documents as \disorders" an...