Bayesian global-local shrinkage methods for regularisation in the high dimension linear model

Bayesian Methods for High Dimensional Linear Models.

In this article, we present a selective overview of some recent developments in Bayesian model and variable selection methods for high dimensional linear models. While most of the reviews in literature are based on conventional methods, we focus on recently developed methods, which have proven to be successful in dealing with high dimensional variable selection. First, we give a brief overview ...

Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data.

BACKGROUND Multivariable confounder adjustment in comparative studies of newly marketed drugs can be limited by small numbers of exposed patients and even fewer outcomes. Disease risk scores (DRSs) developed in historical comparator drug users before the new drug entered the market may improve adjustment. However, in a high dimensional data setting, empirical selection of hundreds of potential ...

Bayesian regularisation methods in a hybrid MLP-HMM system

We have applied Bayesian regularisation methods to multi-layer perceptron (MLP) training in the context of a hybrid MLP– HMM (hidden Markov model) continuous speech recognition system. The Bayesian framework adopted here allows an objective setting of the regularisation parameters, according to the training data. Experiments were carried out on the ARPA Resource Management database.

Bayesian Methods in the Analysis of the General Linear Model

Dimension Theory Local and Global

These survey lectures are devoted to a new subject of the large scale dimension theory which was initiated by Gromov as a part of asymptotic geometry. We are going to enter the large scale world and consider some new concepts, results and examples which are parallel in many cases to the corresponding elements of the standard (local) dimension theory. We start our presentation with the motivations.