论文信息 - STOSS - A Stochastic Simulation System for Bayesian Belief Networks

STOSS - A Stochastic Simulation System for Bayesian Belief Networks

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).

Alexander Gammerman | Zhiyuan Luo

[1] Max Henrion,et al. Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.

[2] Steen Andreassen,et al. MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings , 1987, IJCAI.

[3] Ross D. Shachter. Evaluating Influence Diagrams , 1986, Oper. Res..

[4] Gregory F. Cooper,et al. Current research directions in the development of expert systems based on belief networks , 1989 .

[5] Colin Aitken,et al. Probabilistic reasoning in evidential assessment , 1989 .

[6] David J. Spiegelhalter,et al. Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[7] Judea Pearl,et al. Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[8] Judea Pearl,et al. Evidential Reasoning Using Stochastic Simulation of Causal Models , 1987, Artif. Intell..