---
title: "Exploiting Informative Priors for Bayesian Classification and Regression Trees"
authors:
  - "Nicos Angelopoulos"
  - "James Cussens"
doi: ""
url: "https://ijcai.org/proceedings/2005/122"
pdf: "https://ijcai.org/proceedings/2005/0122.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "641-645"
track: ""
keywords:
---

# Exploiting Informative Priors for Bayesian Classification and Regression Trees

Nicos Angelopoulos, James Cussens

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 641-645.

## Abstract

A general method for defining informative priors on statistical models is presented and applied specifically to the space of classification and regression trees. A Bayesian approach to learning such models from data is taken, with the MetropolisHastings algorithm being used to approximately sample from the posterior. By only using proposal distributions closely tied to the prior, acceptance probabilities are easily computable via marginal likelihood ratios, whatever the prior used. Our approach is empirically tested by varying (i) the data, (ii) the prior and (iii) the proposal distribution. A comparison with related work is given.