Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
an entropy-driven system for construction of probabilistic expert systems from databases
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Stability of Stochastic Approximation under Verifiable Conditions
SIAM Journal on Control and Optimization
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning bayesian networks structure with continuous variables
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
Diagnose the mild cognitive impairment by constructing Bayesian network with missing data
Expert Systems with Applications: An International Journal
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Computational Statistics & Data Analysis
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Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches.