Simulated annealing: theory and applications
Simulated annealing: theory and applications
Learning in graphical models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Enumerating Markov Equivalence Classes of Acyclic Digraph Models
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Bayesian graphical model determination using decision theory
Journal of Multivariate Analysis
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
International Journal of Intelligent Systems - Uncertainty Processing
Bayesian model learning based on a parallel MCMC strategy
Statistics and Computing
A Graphical Representation of Equivalence Classes of AMP Chain Graphs
The Journal of Machine Learning Research
A graphical characterization of the largest chain graphs
International Journal of Approximate Reasoning
Bayesian networks from the point of view of chain graphs
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
An alternative Markov property for chain graphs
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
On Strong Consistency of Model Selection in Classification
IEEE Transactions on Information Theory
Annealed importance sampling for structure learning in Bayesian networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Automated statistical learning of graphical models from data has attained a considerable degree of interest in the machine learning and related literature. Many authors have discussed and/or demonstrated the need for consistent stochastic search methods that would not be as prone to yield locally optimal model structures as simple greedy methods. However, at the same time most of the stochastic search methods are based on a standard Metropolis---Hastings theory that necessitates the use of relatively simple random proposals and prevents the utilization of intelligent and efficient search operators. Here we derive an algorithm for learning topologies of graphical models from samples of a finite set of discrete variables by utilizing and further enhancing a recently introduced theory for non-reversible parallel interacting Markov chain Monte Carlo-style computation. In particular, we illustrate how the non-reversible approach allows for novel type of creativity in the design of search operators. Also, the parallel aspect of our method illustrates well the advantages of the adaptive nature of search operators to avoid trapping states in the vicinity of locally optimal network topologies.