Advertisement

Stoss — A stochastic simulation system for Bayesian belief networks

  • Zhiyuan Luo
  • Alex Gammerman
2. Probabilistic Methods
Part of the Lecture Notes in Computer Science book series (LNCS, volume 521)

Abstract

This paper describes a computational system, called STOSS (STOchastic Simulation System), using the stochastic simulation method to perform probabilistic reasoning for Bayesian belief networks. The system is then applied to an artificial example in the field of forensic science and the results are compared with the calculations obtained using the Causal Probabilistic Reasoning System (CPRS).

Keywords

Bayesian belief networks causal models inference diagrams legal reasoning probabilistic reasoning stochastic simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aitken C.G.G. (1988) In discussion of Lauritzen, S.L. and Spiegelhalter, D.J. Local computations with probabilities on graphical structures and their application to expert systems, J.R. Statist. Soc. (Series B) 50 (1988), 200–201.Google Scholar
  2. 2.
    Aitken C.G.G. and Gammerman A. (1989) Probabilistic reasoning in evidential assessment, Journal of the Forensic Science Society 29 (1989), 303–316.Google Scholar
  3. 3.
    Andreassen S., Woldbye, M., Falck, B. and Andersen, S. (1987) MUNIN — A causal probabilistic network for interpretation of electromyographic findings, Proc. 10th IJCAI 2 (1987), 366–372.Google Scholar
  4. 4.
    Cooper G. (1989) Current research directions in the development of expert systems based on belief networks, Applied Stochastic Models And Data Analysis 5 (1989), 39–52.Google Scholar
  5. 5.
    Gammerman A. and Crabbe W. (1987) Computational models of probabilistic reasoning in expert systems: a causal probabilistic reasoning system, Technical Report 87/16. Computer Science Department, Heriot-Watt University, Edinburgh, U.K.Google Scholar
  6. 6.
    Gammerman A. (1988) In discussion of Lauritzen, S.L. and Spiegelhalter, D.J. Local computations with probabilities on graphical structures and their application to expert systems, J.R. Statist. Soc. (Series B) 50 (1988), 200.Google Scholar
  7. 7.
    Henrion M. (1988) Propagating uncertainty by logic sampling in Bayes' networks, In Uncertainty in Artificial Intelligence 2 (Kanal L.N, Lemmer, J.F. eds.), North-Holland, 149–163.Google Scholar
  8. 8.
    Lauritzen S. and Spiegelhalter D. (1988) Local computations with probabilities on graphical structures and their application to expert systems, J.R. Statist. Soc. (Series B) 50 (1988), 157–224.Google Scholar
  9. 9.
    Luo Z. and Gammerman A. (1989) Probabilistic reasoning using stochastic simulation approach in causal models, Technical Report 89/14. Computer Science Department, Heriot-Watt University, Edinburgh, U.K.Google Scholar
  10. 10.
    Pearl J. (1986) Fusion, propagation, and structuring in belief networks, Artificial Intelligence 29 (1986), 241–288.CrossRefMathSciNetGoogle Scholar
  11. 11.
    Pearl J. (1987) Evidential reasoning using stochastic simulation of causal models, Artificial Intelligence 32 (1987), 245–257.CrossRefMathSciNetGoogle Scholar
  12. 12.
    Shachter R. (1986) Evaluating influence diagrams, Operations Research 34 (1986), 871–882.MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Zhiyuan Luo
    • 1
  • Alex Gammerman
    • 1
  1. 1.Department of Computer ScienceHeriot-Watt UniversityEdinburghU.K.

Personalised recommendations