I have used probabilistic models for a number of years now and over this time I have used different paradigms to build my models, to estimate them from data, and to perform inference and predictions.

Overall I have slowly become a Bayesian; however, it has been a rough journey. When …

An amazing fact in high dimensions is this: Projecting a simple convex set described by a small number of inequalities can create complicated convex set with an exponential number of inequalities.

It is amazing because it contradicts our everyday human experience. We are most familiar with projections of objects in …

Error bars and the \(\pm\)-notation are used to quantitatively convey uncertainty in experimental results. For example, you would often read statements like \(140.7 \textrm{Hz} \pm 2.8 \textrm{ Hz SEM}\) in a paper to report both an experimental average and its uncertainty.

Unfortunately, in many fields (such …

In the last two parts (part one, part two) we looked at the problem of entropy estimation and several popular estimators.

In this final article we will take a look at two Bayesian approaches to the problem.

Bayesian Estimator due to Wolpert and Wolf

The first Bayesian approach to entropy …

This year we (Zoubin, together with David and myself) are again organizing a workshop event for the local Cambridge (UK) machine learning community. The schedule is available at the workshop homepage, Machine Learning in Cambridge 2015, and we also plan to make all talks available as video recordings after the …