Parameterising Bayesian networks

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3339, Issue , 2004, Pages 1101-1107

  Woodberry, Owen ^a   Nicholson, Ann E ^a   Korb, Kevin B ^a   Pollino, Carmel ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER GRAPHICS; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE ENGINEERING; LEARNING SYSTEMS; PARAMETER ESTIMATION; PROBABILITY DISTRIBUTIONS;

BAYESIAN NETWORKS (BN); CONDITIONAL PROBABILITY TABLE (CPT); DOMAIN EXPERTS (DE); PARAMETERIZATION;

COMPUTER NETWORKS;

EID: 22944492706     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30549-1_108     Document Type: Conference Paper

References (12)

1. A. Bhattacharyya. On a measure of divergence between two statistical populations denned by their probability distributions. Bulletin of the Calcutta Mathematics Society, 35:99-110, 1943.
2. F. Brooks. The Mythical Man-Month: Essays on Software Engineering. Addison-Wesley, Reading, MA, second edition, 1995.
3. V. M. H. Coupe and L. C. van der Gaag. Properties of sensitivity analysis of Bayesian belief networks. Annals of Mathematics and Artificial Intelligence, 36:323-356, 2002.
4. M.J. Druzdzel and L.C. van der Gaag. Building probabilistic networks: Where do the numbers come from? IEEE Trans. on Knowledge and Data Engineering, 12(4):481-486, 2001.
5. D. Heckerman and D. Geiger. Learning Bayesian networks. In P. Besnard and S. Hanks, editors, Proceedings of the 11th Annual Conference on Uncertainty in Artificial Intelligence (UAI95), pages 274-284, San Francisco, 1995.
6. K. B. Korb and A. E. Nicholson. Bayesian Artificial Intelligence. Computer Science and Data Analysis. CRC, Boca Raton, 2004.
7. K.B. Laskey and S.M. Mahoney. Network engineering for agile belief network models. IEEE: Transactions on Knowledge and Data Engineering, 12:487-98,2000.
8. A. Nicholson, T. Boneh, T. Wilkin, K. Stacey, L. Sonenberg, and V. Steinle. A case study in knowledge discovery and elicitation in an intelligent tutoring application. In Breese and Koller, editors, Proceedings of the 17th Annual Conference on Uncertainty in Artificial Intelligence (UAI01), pages 386-394, 2001,
9. A. Onisko, M.J. Druzdzel, and H. Wasyluk. Learning Bayesian network parameters from small data sets: application of Noisy-OR gates. In Working Notes of the Workshop on "Bayesian and Causal networks: from inference to data mining." 12th European Conference on Artificial intelligence (ECAI-2000), 2000.
10. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA, 1988.
11. C.S. Wallace and K.B. Korb. Learning linear causal models by MML sampling. In A. Gammerman, editor, Causal Models and Intelligent Data Management. Springer-Verlag, 1999.
12. O. Woodberry, A. Nicholson, K. Korb, and C. Pollino. Parameterising Bayesian networks: A case study in ecological risk assessment. Technical Report 2004/159, School of Computer Science and Software Engineering, Monash University, 2004.