Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors
For practitioners using Bayesian decision trees, this work offers theoretical justification for exploitation decisions, though it is incremental as it extends prior prior work.
The paper provides closed-form non-asymptotic guarantees for when Bayesian model averaging over decision trees with Catalan-exponential priors yields reliable decisions, establishing rational commitment thresholds.
We ask: when do Bayesian model averaging (BMA) weights over decision trees carry sufficient epistemic information to justify committed exploitation of the averaging distribution? We answer this question in closed form for Bayesian decision trees (BDTs) with Dirichlet-Multinomial leaf models and a Catalan-exponential tree-size prior (Schetinin&Jakaite, 2025), establishing a complete non-asymptotic theory of rational commitment thresholds.