| Required or Recommended Resources |
- Merkhofer, M. W. (1987). Quantifying judgmental uncertainty: Methodology, experiences, and insights, IEEE Transactions on Systems, Man, and Cybernetics, 17 (5), 741-752. - Pearl, J. (2000). Causality: models, reasoning, and inference. Cambridge University Press. ISBN 0, 521(77362), 8.- Holyoak, K. J. & Morrison, R. G. (Eds.). (2005). The Cambridge handbook of thinking and reasoning. Cambridge University Press. - Cooke, R. (1991). Experts in uncertainty: opinion and subjective probability in science. Oxford University Press, New York. |
| Recommended Reading List |
1] Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions, Cognitive Science, 27 (3), 453-489.[2] Tversky, A. & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases, Science, 185 (4157), 1124-1131. [3] Wang, H. (2004). Building Bayesian networks: elicitation, evaluation, and learning. (Doctoral dissertation). University of Pittsburgh, Pennsylvania. [4] Weber, P., & Simon, C. (2016). Benefits of Bayesian Network Models. Wiley-ISTE.[5] Xue, S. S., Li, X. C., & Xu, X. Y. (2016). Fault tree and Bayesian network based scraper conveyer fault diagnosis. In Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management 2015 (pp. 783-795). Atlantis Press, Paris.[6] Yazdi, M., Nikfar, F., & Nasrabadi, M. (2017). Failure probability analysis by employing fuzzy fault tree analysis, International Journal of System Assurance Engineering and Management, 8 (2), 1177-1193.[7] Zarei, E., Azadeh, A., Khakzad, N., Aliabadi, M. M., & Mohammadfam, I. (2017). Dynamic safety assessment of natural gas stations using Bayesian network, Journal of Hazardous Materials, 321, 830-840. |
Eskisehir Technical University Info Package