[cgreerrun]

A fast, powerful bayesian model seems like it would be a game-changer. PUCT (the heart of the AlphaZero MCTS that decides which action to choose) really seems setup to model the action choices as a multinomial bayesian inference problem (it already updates the action priors with Dir noise).

Thanks for the link! I don't really know anything about the world of probabilistic trees. I'll check it out.

The only bayesian approach to decision trees I'm familiar with is BART (https://projecteuclid.org/download/pdfview_1/euclid.aoas/127...). I haven't used them, but I'm guessing because it uses MCMC to update the params it's not super fast. I've seen them used in causality applications for partial dependency plots where it's convenient to convey the certainty of a variable's effect.

[gbrown]

Check out the SoftBART method, I think there are interesting optimizations possible there. XBart also looks promising as an approach.