We logically model uncertainty by expanding language without changing logical reasoning rules. We expand the language of set theory by adding new predicate symbols, uncertain membe...
Dealing with interference is one of the primary challenges to solve in the design of protocols for wireless ad-hoc networks. Most of the work in the literature assumes localized o...
This paper presents an efficient algorithm for learning Bayesian belief networks from databases. The algorithm takes a database as input and constructs the belief network structur...
As we devise more complicated prior distributions, will inference algorithms keep up? We highlight a negative result in computable probability theory by Ackerman, Freer, and Roy (...
This paper introduces and describes an innovative modelling approach which utilises models that are synthesised through approximate calculations of user actions and extensive repr...