Every complex system can be determined by a causal probabilistic network without cycles and every such network determines a Markov field

  • Ulrich G. Oppel
Contributed Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 548)

Key words

Causal probabilistic network belief net directed Markov field expert system convergence of causal probabilistic networks sensitivity analysis of causal probabilistic networks HUGIN 


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  1. [1]
    Ash, R.B.: Real Analysis and Probability. Academic Press: New York, 1972.Google Scholar
  2. [2]
    Billingsley, P.: Convergence of Probability Measures. Wiley: New York. 1968.Google Scholar
  3. [3]
    Jensen, F.V.: Lauritzen, S.L.: Oleson, K.G.: Bayesian updating in causal probabilistic networks by local computations. Computational Statistics Quaterly 4 (1990), 269–282.Google Scholar
  4. [4]
    Lauritzen, S.L.: Dawid, A.P.: Larsen, B.N.: Leimer, H.-G.: Independence properties of directed Markov fields. Networks 20 (1990), 491–505.Google Scholar
  5. [5]
    Lauritzen, S.L.: Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their applications to expert systems. J. Royal Stat. Soc. B 50 (2) (1988). 157–224.Google Scholar
  6. [6]
    Neveu, J.: Bases Mathématiques du Calcul des Probabilités. Masson et Cie.: Paris, 1964.Google Scholar
  7. [7]
    Oppel, U.G.: Kausal-probabilistische Expertensysteme. Vorlesungsskript. Mathematisches Institut der L-M-Universität München, 1991.Google Scholar
  8. [8]
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann: San Mateo, CA. USA; 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Ulrich G. Oppel
    • 1
  1. 1.Mathematisches Institut der Ludwig-Maximilians-UniversitätMünchen 2Fed. Rep. Germany

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