"Rich Mixtures for Multidimensional Smoothing" joint with Bill Szewczyk Abstract: Mixture models, which include kernel estimators, are widely used to model complex densities; however, one is faced with the challenge of determining an appropriate number of components. Kernel methods are a special case of mixture models but are generally viewed as rather susceptible to the curse of dimensionality. Locally adaptive kernel estimators are highly desirable in multiple dimensions but few practical algorithms exist for such estimation. Thus mixture models may be viewed as a more parsimonious kernel estimator but without trying to get the number of components exactly correct. In this talk we describe how a mixture estimate may be calibrated by starting with a kernel estimator. The IPRA algorithm sequentially fits mixture models.