Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A random polynomial-time algorithm for approximating the volume of convex bodies
Journal of the ACM (JACM)
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Statistics and Computing
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Parallell interacting MCMC for learning of topologies of graphical models
Data Mining and Knowledge Discovery
From sampling to model counting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Computing posterior probabilities of structural features in Bayesian networks
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Theory refinement on Bayesian networks
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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We present a new sampling approach to Bayesian learning of the Bayesian network structure. Like some earlier sampling methods, we sample linear orders on nodes rather than directed acyclic graphs (DAGs). The key difference is that we replace the usual Markov chain Monte Carlo (MCMC) method by the method of annealed importance sampling (AIS). We show that AIS is not only competitive to MCMC in exploring the posterior, but also superior to MCMC in two ways: it enables easy and efficient parallelization, due to the independence of the samples, and lower-bounding of the marginal likelihood of the model with good probabilistic guarantees. We also provide a principled way to correct the bias due to order-based sampling, by implementing a fast algorithm for counting the linear extensions of a given partial order.