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
A logic for reasoning about evidence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Tagging of name records for genealogical data browsing
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
An approach to hybrid probabilistic models
International Journal of Approximate Reasoning
A General Model for Epistemic State Revision using Plausibility Measures
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Belief Revision through Forgetting Conditionals in Conditional Probabilistic Logic Programs
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
An edge deletion semantics for belief propagation and its practical impact on approximation quality
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Revising imprecise probabilistic beliefs in the framework of probabilistic logic programming
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
A logic for reasoning about evidence
Journal of Artificial Intelligence Research
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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.