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Probabilistic Reasoning and
Bayesian Belief Networks

A.Gammerman (ed), Alfred Waller Ltd, 1995

Probalistic Reasoning and Baysian Belief Networks cover The rapid growth and challenging nature of the data and information facing many industries and businesses have created a need for mathematical models and methods to extract knowledge from such data. This has led to the creation of entirely new classes of computational and statistical tools. These tools include intelligent computer systems, which use heuristics to improve their decision-making procedures in the light of given examples. This book summarises some important work in the development of computational models of Bayesian Belief Networks for use in this process, and their application to medicine, transport and defence.


  1. From Bayesian networks to causal networks, J.Pearl.
  2. Exact and approximate algorithms and their implementations in mixed graphical models, A.Gammerman, Z.Luo, C.G.G.Aitken, M.J.Brewer.
  3. Models and modelling in context, A.P.Dempster, E.N.Brown.
  4. Modelling ignorance in uncertainty theories, P.P.Shenoy.
  5. Choosing network complexity, B.D.Ripley.
  6. drHugin: A system for hypothesis driven data request, F.V.Jensen, J.Liang.
  7. An efficient graphical algorithm for updating the estimates of the dispersal of gaseous waste after an accidental release, J.Q.Smith, S.French, D.Ranyard.
  8. Graphical representation of a network traffic model, J.Whittaker.
  9. A C++ class library for building Bayesian Belief Networks, R.G, Cowell.
  10. Smoothing noisy signals with Bayesian Networks, R.Bellazzi, G.De Nicolao.
  11. Efficient multiple-disorder diagnosis by strategic focusing, L.van der Gaag, M.Wessels.
  12. Weighted inference rules and Bayesian belief networks, B.S.Todd.
  13. On the Idiot vs. Proper Bayes Approach in clinical diagnostic systems, H.J.Lenz.
  14. Constructing computationally efficient Bayesian models via unsupervised clustering, P.Myllymäki, H.Tirri.
  15. Bayesian graphical models of the natural history of HIV-infection, C.Berzuini, C.Gobbi.

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