Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the logic of iterated belief revision
Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
On Two Pseudo-Paradoxes in Bayesian Analysis
Annals of Mathematics and Artificial Intelligence
A distance measure for bounding probabilistic belief change
Eighteenth national conference on Artificial intelligence
A distance measure for bounding probabilistic belief change
International Journal of Approximate Reasoning
Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
A Comparison between Two Approaches to Threat Evaluation in an Air Defense Scenario
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
A General Framework for Revising Belief Bases Using Qualitative Jeffrey's Rule
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
A Framework for Iterated Belief Revision Using Possibilistic Counterparts to Jeffrey's Rule
Fundamenta Informaticae - Methodologies for Intelligent Systems
Knowledge representation, communication, and update in probability-based multiagent systems
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Jeffrey's rule of conditioning in a possibilistic framework
Annals of Mathematics and Artificial Intelligence
Belief revision of product-based causal possibilistic networks
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
Probabilistic Belief Contraction
Minds and Machines
Revision over partial pre-orders: a postulational study
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Expert Systems with Applications: An International Journal
Syntactic computation of hybrid possibilistic conditioning under uncertain inputs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We revisit the problem of revising probabilistic beliefs using uncertain evidence, and report results on several major issues relating to this problem: how should one specify uncertain evidence? How should one revise a probability distribution? How should one interpret informal evidential statements? Should, and do, iterated belief revisions commute? And what guarantees can be offered on the amount of belief change induced by a particular revision? Our discussion is focused on two main methods for probabilistic revision: Jeffrey's rule of probability kinematics and Pearl's method of virtual evidence, where we analyze and unify these methods from the perspective of the questions posed above.