Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the hardness of approximate reasoning
Artificial Intelligence
A machine program for theorem-proving
Communications of the ACM
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Introduction to Digital Logic Design
Introduction to Digital Logic Design
Evidence Absorption and Propagation through Evidence Reversals
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Approximating MAP using Local Search
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Counting Models Using Connected Components
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
A compiler for deterministic, decomposable negation normal form
Eighteenth national conference on Artificial intelligence
Algorithms and Complexity Results for #SAT and Bayesian Inference
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
A differential semantics for jointree algorithms
Artificial Intelligence
Sensitivity analysis in Bayesian networks: from single to multiple parameters
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Exploiting random walk strategies in automated reasoning
Exploiting random walk strategies in automated reasoning
AND/OR search spaces for graphical models
Artificial Intelligence
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Compiling Bayesian networks using variable elimination
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
DPLL with a trace: from SAT to knowledge compilation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Compiling relational Bayesian networks for exact inference
International Journal of Approximate Reasoning
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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
Value elimination: bayesian inference via backtracking search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Solving MAP exactly using systematic search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Heuristics for fast exact model counting
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
A new approach to model counting
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Encoding CNFs to empower component analysis
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Learning to assign degrees of belief in relational domains
Machine Learning
Online Rule Learning via Weighted Model Counting
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Solving #SAT and Bayesian inference with backtracking search
Journal of Artificial Intelligence Research
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Join-graph propagation algorithms
Journal of Artificial Intelligence Research
Journal of Automated Reasoning
Exploiting structure in weighted model counting approaches to probabilistic inference
Journal of Artificial Intelligence Research
Marginalization without summation: exploiting determinism in factor algebra
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Relax, compensate and then recover
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
Compiling bayesian networks for parameter learning based on shared BDDs
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Importance sampling-based estimation over AND/OR search spaces for graphical models
Artificial Intelligence
Local structure and determinism in probabilistic databases
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
An efficient Monte-Carlo algorithm for pricing combinatorial prediction markets for tournaments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
SDD: a new canonical representation of propositional knowledge bases
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Lifted probabilistic inference by first-order knowledge compilation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Journal of Biomedical Informatics
The Multivariate Algorithmic Revolution and Beyond
Compiling probabilistic graphical models using sentential decision diagrams
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Oblivious bounds on the probability of boolean functions
ACM Transactions on Database Systems (TODS)
Artificial Intelligence: From programs to solvers
AI Communications - ECAI 2012 Turing and Anniversary Track
Probabilistic inference with noisy-threshold models based on a CP tensor decomposition
International Journal of Approximate Reasoning
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A recent and effective approach to probabilistic inference calls for reducing the problem to one of weighted model counting (WMC) on a propositional knowledge base. Specifically, the approach calls for encoding the probabilistic model, typically a Bayesian network, as a propositional knowledge base in conjunctive normal form (CNF) with weights associated to each model according to the network parameters. Given this CNF, computing the probability of some evidence becomes a matter of summing the weights of all CNF models consistent with the evidence. A number of variations on this approach have appeared in the literature recently, that vary across three orthogonal dimensions. The first dimension concerns the specific encoding used to convert a Bayesian network into a CNF. The second dimensions relates to whether weighted model counting is performed using a search algorithm on the CNF, or by compiling the CNF into a structure that renders WMC a polytime operation in the size of the compiled structure. The third dimension deals with the specific properties of network parameters (local structure) which are captured in the CNF encoding. In this paper, we discuss recent work in this area across the above three dimensions, and demonstrate empirically its practical importance in significantly expanding the reach of exact probabilistic inference. We restrict our discussion to exact inference and model counting, even though other proposals have been extended for approximate inference and approximate model counting.