Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Learning and robust learning of product distributions
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Real-world applications of Bayesian networks
Communications of the ACM
Dynamic Programming Treatment of the Travelling Salesman Problem
Journal of the ACM (JACM)
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
An Algebraic Model for Combinatorial Problems
SIAM Journal on Computing
Local computation with valuations from a commutative semigroup
Annals of Mathematics and Artificial Intelligence
Computational aspects of the Mobius transformation
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
On inclusion-driven learning of bayesian networks
The Journal of Machine Learning Research
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A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The generalized distributive law
IEEE Transactions on Information Theory
Computational aspects of Bayesian partition models
ICML '05 Proceedings of the 22nd international conference on Machine learning
Evolved bayesian networks as a versatile alternative to partin tables for prostate cancer management
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Parallell interacting MCMC for learning of topologies of graphical models
Data Mining and Knowledge Discovery
Structure learning of Bayesian networks using constraints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Bayesian learning of Bayesian networks with informative priors
Annals of Mathematics and Artificial Intelligence
Automatic linear causal relationship identification for financial factor modeling
Expert Systems with Applications: An International Journal
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure
The Journal of Machine Learning Research
Bayesian discovery of linear acyclic causal models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Exact structure discovery in Bayesian networks with less space
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Computing posterior probabilities of structural features in Bayesian networks
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Learning locally minimax optimal Bayesian networks
International Journal of Approximate Reasoning
A space-time tradeoff for permutation problems
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Globally optimal structure learning of Bayesian networks from data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Efficient Structure Learning of Bayesian Networks using Constraints
The Journal of Machine Learning Research
Parallel Algorithm for Learning Optimal Bayesian Network Structure
The Journal of Machine Learning Research
Ancestor relations in the presence of unobserved variables
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Bayesian learning with mixtures of trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Searching optimal bayesian network structure on constraint search space: super-structure approach
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
Learning optimal Bayesian networks using A* search
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Learning bayesian networks from Markov random fields: An efficient algorithm for linear models
ACM Transactions on Knowledge Discovery from Data (TKDD)
An experimental comparison of hybrid algorithms for bayesian network structure learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
A multi-parent search operator for bayesian network building
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
New skeleton-based approaches for Bayesian structure learning of Bayesian networks
Applied Soft Computing
Parallel globally optimal structure learning of Bayesian networks
Journal of Parallel and Distributed Computing
Finding optimal Bayesian networks using precedence constraints
The Journal of Machine Learning Research
Learning linear cyclic causal models with latent variables
The Journal of Machine Learning Research
Annealed importance sampling for structure learning in Bayesian networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Segregating event streams and noise with a Markov renewal process model
The Journal of Machine Learning Research
Learning optimal bayesian networks: a shortest path perspective
Journal of Artificial Intelligence Research
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Learning a Bayesian network structure from data is a well-motivated but computationally hard task. We present an algorithm that computes the exact posterior probability of a subnetwork, e.g., a directed edge; a modified version of the algorithm finds one of the most probable network structures. This algorithm runs in time O(n 2n + nk+1C(m)), where n is the number of network variables, k is a constant maximum in-degree, and C(m) is the cost of computing a single local marginal conditional likelihood for m data instances. This is the first algorithm with less than super-exponential complexity with respect to n. Exact computation allows us to tackle complex cases where existing Monte Carlo methods and local search procedures potentially fail. We show that also in domains with a large number of variables, exact computation is feasible, given suitable a priori restrictions on the structures; combining exact and inexact methods is also possible. We demonstrate the applicability of the presented algorithm on four synthetic data sets with 17, 22, 37, and 100 variables.