D. M. Titterington

TL;DR: This course discusses Mathematical Aspects of Mixtures, Sequential Problems and Procedures, and Applications of Finite Mixture Models.

TL;DR: The deviance information criterion is reassessed for missing data models, testing the behaviour of variousextensions in the cases of mixture and random models.

TL;DR: In this article, the authors define and compute asymptotically optimal difference sequences for estimating error variance in homoscedastic nonparametric regression, and provide substantial improvements over the suboptimal sequences commonly used in practice.

TL;DR: The method of regularization is portrayed as providing a compromise between fidelity to the data and smoothness, with the tradeoff being determined by a scalar smoothing parameter.

TL;DR: In this paper, a hierarchical prior model is proposed to deal with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context, and a sample from the full joint distribution of all unknown variables is generated, which can be used as a basis for a thorough presentation of many aspects of the posterior distribution.