Angelopoulos, N. and Cussens, J. (2005) Exploiting informative priors for Bayesian classification and regression trees. In: Pack Kaelbling, L. and Saffiotti, A., (eds.) Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. IJCAI-05, July 30-August 5, 2005, Edinburgh, Scotland. Professional Book Center , pp. 641-646. ISBN 0938075934Full text not available from this repository.
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 Metropolis- Hastings 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.
|Item Type:||Proceedings Paper|
|Academic Units:||The University of York > Computer Science (York)|
|Depositing User:||York RAE Import|
|Date Deposited:||24 Mar 2009 16:53|
|Last Modified:||24 Mar 2009 16:53|
|Publisher:||Professional Book Center|
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