Mathematical Sciences: Workshop on Statistical Mixture Modelling

点击翻译按钮获取中文摘要

PROPOSAL: DMS 94-24490 PI: Mike West TITLE: WORKSHOP ON STATISTICAL MIXTURE MODELLING ABSTRACT: The Workshop provides a forum for reviewing and publicizing widely dispersed research activities in mixture modelling, to stimulate fertilization of theoretical, methodological and computational research directions for the near future, and, significantly, to focus attention on the burgeoning arena of applied problems in complex stochastic systems that are inherently structured in mixture terms. The Workshop will heavily concentrate in topical problems with strong cross-disciplinary and computational components, featuring mixture problems in areas such as density estimation, regression and time series; mixtures in statistical image modelling and analysis, neural networks and signal processing; graphical models and networks; stochastic simulation for mixture analysis; clustering and classification problems; model selection and combination; alternative approaches to inference in mixtures; latent variables and incomplete data problems; and applications of mixtures in various scientific areas. The Workshop is held in recognition of the recent growth and development in the theory and, in particular, applications of statistical methods based on mixtures of distributions. Mixture models play key roles in many complex modelling and inference problems in science, engineering and social sciences, and advances in computational statistical technology have, and continue to, promote their wider use. This Workshop provides a forum for reviewing and publicizing widely dispersed research activities in mixture modelling, stimulating fertilization of theoretical, methodological and computational research directions for the near future, and focusing attention on the wide variety of significant applied problems in complex stochastic systems that are inherently structured in mixture terms. The workshop will bring together senior researchers, new researchers and students from various backgrounds to prom ote exchange and interactions on the frontiers of statistical mixture modelling, to highlight the development of statistical technology across these fields, to promote interchanges between researchers in various applied fields, and to highlight the utility of mixture models in diverse fields such as genetics, astronomy and physics, the neuro-sciences, and others.