Inference with separately specified sets of probabilities in credal networks

Authors:
José Carlos Ferreira da Rocha;Fabio Gagliardi Cozman
Affiliations:
Escola Politcnica, Univ. de São Paulo and Univ. Estadual de Ponta Grossa, Ponta Grossa, PR Brazil;Escola Politécnica, Univ. de São Paulo, São Paulo, SP, Brazil
Venue:
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Year:
2002

Citing 8
Cited 10

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian logic

Decision Support Systems - Special issue on logic modeling
2U: an exact interval propagation algorithm for polytrees with binary variables

Artificial Intelligence
Credal networks

Artificial Intelligence
Strong Conditional Independence for Credal Sets

Annals of Mathematics and Artificial Intelligence
Separation Properties of Sets of Probability Measures

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms

UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Independence concepts for convex sets of probabilities

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Evidence Propagation in Credal Networks: An Exact Algorithm Based on Separately Specified Sets of Probability

SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Propositional and relational Bayesian networks associated with imprecise and qualitative probabilistic assessments

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Updating beliefs with incomplete observations

Artificial Intelligence
Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks

International Journal of Approximate Reasoning
Decision-theoretic specification of credal networks: A unified language for uncertain modeling with sets of Bayesian networks

International Journal of Approximate Reasoning
The inferential complexity of Bayesian and credal networks

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Graphical models for imprecise probabilities

International Journal of Approximate Reasoning
Inference in credal networks: branch-and-bound methods and the A/R+ algorithm

International Journal of Approximate Reasoning
Inference in polytrees with sets of probabilities

UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Making decisions using sets of probabilities: updating, time consistency, and calibration

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present new algorithms for inference in credal networks -- directed acyclic graphs associated with sets of probabilities. Credal networks are here interpreted as encoding strong independence relations among variables. We first present a theory of credal networks based on separately specified sets of probabilities. We also show that inference with polytrees is NP-hard in this setting. We then introduce new techniques that reduce the computational effort demanded by inference, particularly in polytrees, by exploring separability of credal sets.