Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Importance sampling in Bayesian networks using probability trees
Computational Statistics & Data Analysis
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Simplifying explanations in Bayesian belief networks
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Technologies for constructing intelligent systems
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
Explanation, irrelevance and statistical independence
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Defining explanation in probabilistic systems
UAI'97 Proceedings of the Thirteenth 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
An efficient approach for finding the MPE in belief networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Solving MAP exactly using systematic search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A general framework for generating multivariate explanations in Bayesian networks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Most Relevant Explanation: properties, algorithms, and evaluations
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Most relevant explanation in Bayesian networks
Journal of Artificial Intelligence Research
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This paper proposes a new approach to the problem of ob- taining the most probable explanations given a set of observations in a Bayesian network. The method provides a set of possibilities ordered by their probabilities. The main novelties are that the level of detail of each one of the explanations is not uniform (with the idea of being as simple as possible in each case), the explanations are mutually exclusive, and the number of required explanations is not fixed (it depends on the particular case we are solving). Our goals are achieved by means of the construction of the so called explanation tree which can have asym- metric branching and that will determine the different possibilities. This paper describes the procedure for its computation based on information theoretic criteria and shows its behaviour in some simple examples.