Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Probability updating using second order probabilities and conditional event algebra
Information Sciences: an International Journal
Linguistic Bayesian Networks for reasoning with subjective probabilities in forensic statistics
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Towards qualitative approaches to Bayesian evidential reasoning
Proceedings of the 11th international conference on Artificial intelligence and law
Argument diagramming in logic, law and artificial intelligence
The Knowledge Engineering Review
Knowledge based crime scenario modelling
Expert Systems with Applications: An International Journal
Refining reasoning in qualitative probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Much work on probabilistic evidential reasoning for crime investigation employs probabilities that express subjective expert beliefs. This use of subjective probabilities is inevitable for several reasons, including lack of data, non-specificity of phenomena and fuzziness of concepts in this domain. Numerous representation formalisms and corresponding inference mechanisms have been developed to capture and reason with the intrinsic vagueness in subjective probabilities. In the literature, these schemes are largely presented as though they are diametrically opposed to one another. This paper critically examines what aspects of vagueness are captured by these different approaches. It demonstrates that they are concerned with different aspects of vagueness. This leads to a proposal of a method to combine the different approaches.