Exploiting Informative Priors for Bayesian Classification and Regression Trees
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.
@inproceedings{ijcai2005p122,
title = {Exploiting Informative Priors for Bayesian Classification and Regression Trees},
author = {Angelopoulos, Nicos and Cussens, James},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {641--645},
year = {2005},
url = {https://ijcai.org/proceedings/2005/122},
}
TY - CPAPER TI - Exploiting Informative Priors for Bayesian Classification and Regression Trees AU - Angelopoulos, Nicos AU - Cussens, James T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 641 EP - 645 UR - https://ijcai.org/proceedings/2005/122 ER -Copied
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