Abstract Decision trees (DT) are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like CART, ID3, random forest, boosted miss a probabilistic version that encodes prior assumptions about tree structures shares statistical strength between node parameters. Existing work on Bayesian DT depends Markov Chain Monte Carlo (MCMC), wh...