Bayesian Networks for enterprise risk assessment

Physica A: Statistical Mechanics and its Applications

Volumn 382, Issue 1, 2007, Pages 22-28

  Bonafede, C E ^a   Giudici, P ^a  

^a UNIVERSITY OF PAVIA   (Italy)

Author keywords

Bayesian Networks; Enterprise risk assessment; Mutual information

Indexed keywords

INTEGRATION; PROBABILITY; RISK ANALYSIS; RISK ASSESSMENT;

MARGINAL CORRELATIONS; PROBABILITY COMBINATION; QUALITATIVE DATA;

BAYESIAN NETWORKS;

EID: 34249823638     PISSN: 03784371     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.physa.2007.02.065     Document Type: Article

References (16)

1. Cowell R.G., Dawid A.P., Lauritzen S.L., and Spiegelhalter D.J. Probabilistic Networks and Expert Systems (1999), Springer, New York, USA
2. Jensen F.V. Bayesian Networks and Decision Graphs (2001), Springer, New York, USA
3. Giudici P. Applied Data Mining (2003), Wiley, West Sussex, England
4. K.P. Murphy, An introduction to graphical models, May 10, from 〈http://www.cs.ubc.ca〉, 2001.
5. Druzdzel M.J., and van der Gaag L.C. Building probabilistic networks: where do the numbers come from?. IEEE Trans. Knowledge Data Eng. 12 4 (2000) 481-486
6. Jaakkola T.S., and Jordan M.I. Variational probabilistic inference and the QMR-DT database. J. Artif. Intell. Res. 10 (1999) 291-322
7. D.A. Wiegmann, Developing a methodology for eliciting subjective probability estimates during expert evaluations of safety interventions: application for bayesian belief networks, Aviation Human Factors Division, October, from 〈http://www.humanfactors.uiuc.edu〉, 2005.
8. A.R. Daneshkhah, Uncertainty in probabilistic risk assessment: a review, The University of Sheffield, August 9, from 〈http://www.shef.ac.uk/beep/publications.html〉, 2004.
9. Druzdzel M.J., and van der Gaag L.C. Elicitation of probabilities for belief networks: combining qualitative and quantitative information. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (1995) 141-148
10. Zagorecki A., and Druzdzel M. An empirical study of probability elicitation under noisy-OR assumption. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004) (2004) 800-885
11. Francisco J.D., and Severino F.G. Efficient Computation for the Noisy MAX. Int. J. Intell. Systems 18 (2003) 165-177
12. Agnieszka O., Druzdzel M., and Wasyluk H. Learning Bayesian network parameters from small data sets: application of Noisy-OR gates. Int. J. Approximate Reasoning 27 (2001) 165-182
13. C. Alexander (Ed.), Operational Risk, Regulation Analysis and Management, Prentice-Hall, London, England, 2003.
14. Cruz M.G. Modeling, Measuring and Hedging Operational Risk (2002), Wiley, West Sussex, England
15. Williamson J. Approximating discrete probability distributions with Bayesian networks. IEEE Trans. Inform. Theory 14 3 (2000) 462-467
16. Kleiter G.D., and Jirousek R. Learning Bayesian networks under the control of mutual information. Proceedings in Information Processing and Management of Uncertainty in Knowledge-Based Systems (1996) 985-990