THE VITAL STATISTICIAN

The Embodied Statistician

How do infants, children, and adults learn grammatical rules from the mere observation of grammatically structured sequences? We present an embodied hypothesis that (a) people covertly imitate stimuli; (b) imitation tunes the particular neuromuscular systems used in the imitation, facilitating transitions between the states corresponding to the successive grammatical stimuli; and (c) the discri...

Towards a Neural Statistician

An efficient learner is one who reuses what they already know to tackle a new problem. For a machine learner, this means understanding the similarities amongst datasets. In order to do this, one must take seriously the idea of working with datasets, rather than datapoints, as the key objects to model. Towards this goal, we demonstrate an extension of a variational autoencoder that can learn a m...

University of Groningen The statistician

Essential Geodesics for the Spatial Statistician

Statisticians analyzing spatial data often need to detect and model associations based upon distances and angles. Computation of distances and angles are needed for exploratory purposes, as well as for starting estimation algorithms. When the data come from locations on the spherical earth, application of Euclidean metrics for computing these is not straightforward. This article clarifies such ...

A Statistician Plays Darts

Darts is enjoyed both as a pub game and as a professional competitive activity. Yet most players aim for the highest scoring region of the board, regardless of their skill level. By modeling a dart throw as a 2-dimensional Gaussian random variable, we show that this is not always the optimal strategy. We develop a method, using the EM algorithm, for a player to obtain a personalized heatmap, wh...