Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A logic-based analysis of Dempster-Shafer theory
International Journal of Approximate Reasoning
Search-based methods to bound diagnostic probabilities in very large belief nets
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
The sensitivity of belief networks to imprecise probabilities: an experimental investigation
Artificial Intelligence - Special volume on empirical methods
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
An optimal approximation algorithm for Bayesian inference
Artificial Intelligence
An Introduction to Variational Methods for Graphical Models
An Introduction to Variational Methods for Graphical Models
Probabilistic partial evaluation: exploiting rule structure in probabilistic inference
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Mini-buckets: a general scheme for generating approximations in automated reasoning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
When do numbers really matter?
Journal of Artificial Intelligence Research
Active tuples-based scheme for bounding posterior beliefs
Journal of Artificial Intelligence Research
Representing and combining partially specified CPTs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks with restricted causal interactions
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
When do numbers really matter?
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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There is evidence that the numbers in probabilistic inference don't really matter. This paper considers the idea that we can make a probabilistic model simpler by making fewer distinctions. Unfortunately, the level of a Bayesian network seems too coarse; it is unlikely that a parent will make little difference for all values of the other parents. In this paper we consider an approximation scheme where distinctions can be ignored in some contexts, but not in other contexts. We elaborate on a notion of a parent context that allows a structured context-specific decomposition of a probability distribution and the associated probabilistic inference scheme called probabilistic partial evaluation (Poole 1997). This paper shows a way to simplify a probabilistic model by ignoring distinctions which have similar probabilities, a method to exploit the simpler model, a bound on the resulting errors, and some preliminary empirical results on simple networks.