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Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors

arXiv 2606.01340 ↗First posted 31 May 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

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.

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References · 5

  1. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  2. 2021User-friendly introduction to PAC-Bayes boundscited by 234
  3. 2019A Primer on PAC-Bayesian Learningcited by 130
  4. 2016PAC-Bayesian Theory Meets Bayesian Inferencecited by 87
  5. 2013Understanding predictive information criteria for Bayesian modelscited by 51

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