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Divide, Conquer, Combine Bayesian Decision Tree Sampling
A challenge for existing MCMC approaches is proposing joint changes in both the tree structure and the decision parameters that result in efficient sampling.
RJHMC-Tree for Exploration of the Bayesian Decision Tree Posterior
This paper is directed towards learning decision trees from data using a Bayesian approach, which is challenging due to the potentially enormous parameter space required to span all tree models.
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