or</em> probability distribution in a Bayesian sense, aiming to arrive at a <em>posterior</em> distribution with which to train future networks using SBLE-PUCT search (which effectively collapses to AZ-PUCT during fast self-play games). After 800 simulations, the number of visits from the root to each possible move form a new distribution as the training target. AZ-PUCT has a desirable property in that while the best move will eventually get most of the visits, at lower simulation counts the distribution ju