This paper reviews recent ideas in Bayesian classification modelling via partitioning. These methods provide predictive estimates for class assignments using averages of a sample of models generated from the posterior distribution of the model parameters. We discuss modifications to the basic approach more suitable for problems when there are many predictor variables and/or a large training smple. © 2002 Elsevier Science B.V. All rights reserved.

Original publication

DOI

10.1016/S0167-9473(01)00073-1

Type

Conference

Publication Date

28/02/2002

Volume

38

Pages

475 - 485