Diagnostic reasoning based on a genetic algorithm operating in a Bayesian belief network

Pattern Recognition Letters

Volumn 17, Issue 10, 1996, Pages 1047-1055

  Gelsema, E S ^a  

^a ERASMUS UNIVERSITY ROTTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CORRELATION THEORY; DIAGNOSIS; DISEASES; MATHEMATICAL MODELS; PROBABILITY;

BELIEF NETWORK;

NEURAL NETWORKS;

EID: 0030565411     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/0167-8655(96)00068-2     Document Type: Article

References (10)

1. Cooper, G.F. (1990). The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42, 393-405.
2. Dagun, P. and M. Luby (1993). Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence 60, 141-153.
3. Davis, L. (1991). Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.
4. Gelsema, E.S. (1995). Abductive reasoning in Bayesian belief networks using a genetic algorithm. Pattern Recognition Lett. 16, 865-871.
5. Neapolitan, R.E. (1989). Probabilistic Reasoning in Expert Systems: Theory and Algorithms. Wiley, New York.
6. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo.
7. Peng, Y. and J.A. Reggia (1987a). A probabilistic causal model for diagnostic problem solving, Part 1: Integrating symbolic causal inference with numeric probabilistic inference. IEEE Trans. Syst. Man Cybernet. 17, 146-162.
8. Peng, Y. and J.A. Reggia (1987b). A probabilistic causal model for diagnostic problem solving, Part 2: Diagnostic strategy. IEEE Trans. Syst. Man Cybernet. 17, 395-406.
9. Sachs, L. (1984). Angewandte Statistik. Springer, Berlin.
10. Shimony, S.E. (1994). Finding MAP'S for belief networks is NP-hard. Artificial Intelligence 68, 399-410.