Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Context-specific sign-propagation in qualitative probabilistic networks
Artificial Intelligence
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Qualitative probability and order of magnitude reasoning
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
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 a formal account of reasoning about evidence: argumentation schemes and generalisations
Artificial Intelligence and Law - Law, logic and defeasibility
Refining reasoning in qualitative probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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
Evaluating cases in legal disputes as rival theories
JSAI-isAI'09 Proceedings of the 2009 international conference on New frontiers in artificial intelligence
Hybrid integration of reasoning techniques in suspect investigation
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Compositional Bayesian modelling for computation of evidence collection strategies
Applied Intelligence
On extracting arguments from Bayesian network representations of evidential reasoning
Proceedings of the 13th International Conference on Artificial Intelligence and Law
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A crucial aspect of evidential reasoning in crime investigation involves comparing the support that evidence provides for alternative hypotheses. Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be employed for this purpose. However, the specification of BNs requires conditional probability tables describing the uncertain processes under evaluation. When these processes are poorly understood, it is necessary to rely on subjective probabilities provided by experts. Accurate probabilities of this type are normally hard to acquire from experts. Recent work in qualitative reasoning has developed methods to perform probabilistic reasoning using coarser representations. However, the latter types of approaches are too imprecise to compare the likelihood of alternative hypotheses. This paper examines this shortcoming of the qualitative approaches when applied to the aforementioned problem, and identifies and integrates techniques to refine them.