Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Stochastic sensitivity analysis using fuzzy influence diagrams
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
From approximative to descriptive fuzzy classifiers
IEEE Transactions on Fuzzy Systems
Probabilistic abductive computation of evidence collection strategies in crime investigation
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
APCCM '07 Proceedings of the fourth Asia-Pacific conference on Comceptual modelling - Volume 67
Towards qualitative approaches to Bayesian evidential reasoning
Proceedings of the 11th international conference on Artificial intelligence and law
Conceptions of Vagueness in Subjective Probability for Evidential Reasoning
Proceedings of the 2009 conference on Legal Knowledge and Information Systems: JURIX 2009: The Twenty-Second Annual Conference
Compositional Bayesian modelling for computation of evidence collection strategies
Applied Intelligence
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Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be used to infer the probability of defence and prosecution statements based on forensic evidence. This is an important development as it helps to quantify the meaning of forensic expert testimony during court proceedings, for example, that there is "strong support" for the defence or prosecution position. Due to the lack of experimental data, inferred probabilities often rely on subjective probabilities provided by experts. Because these are based on informed guesses, it is very difficult to express them accurately with precise numbers. Yet, conventional BNs can only employ probabilities expressed as real numbers. To address this issue, this paper presents a novel extension of probability theory. This allow the expression of subjective probabilities as fuzzy numbers, which more faithfully reflect expert opinion. By means of practical a example, it will be shown that the accurate representation of this lack of precision in reasoning with subjective probabilities has important implications for the overall result.