Tractable Bayesian Learning of Tree Belief Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Learning with mixtures of trees
The Journal of Machine Learning Research
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
On the complexity of computing determinants
Computational Complexity
TAN Classifiers Based on Decomposable Distributions
Machine Learning
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We present a Bayesian method for learning mixtures of graphical models. In particular, we focus on data clustering with a tree-structured model for each cluster. We use a Markov chain Monte Carlo method to draw a sample of clusterings, while the likelihood of a clustering is computed by exact averaging over the model class, including the dependency structure on the variables. Experiments on synthetic data show that this method usually outperforms the expectation–maximization algorithm by Meilă and Jordan [1] when the number of observations is small (hundreds) and the number of variables is large (dozens). We apply the method to study how much single nucleotide polymorphisms carry information about the structure of human populations.