In this paper we present a simple and straightforward approach to the problem of single-trial classification of event-related potentials (ERP) in EEG. We exploit the well-known fact that event-related drifts in EEG potentials can well be observed if averaged over a sufficiently large number of trials. We propose to use the average signal and its variance as a generative model for each event cla...

Jarno Vanhatalo, Pasi Jylänki, and Aki Vehtari. Gaussian process regression with Student-t likelihood. In NIPS, pages 1910–1918, 2009. Amar Shah, Andrew Gordon Wilson, and Zoubin Ghahramani. Student-t processes as alternatives to Gaussian processes. In AISTATS, pages 877–885, 2014. Anthony O'Hagan. On outlier rejection phenomena in Bayes inference. Journal of the Royal Statistical Society. Seri...

People can create impressive new objects: an architect designs new houses, a cook designs new recipes, and an entrepreneur designs new companies. On the other hand, computers are better known for consistency than creativity, and thus the computational basis of creativity remains mysterious. Recent work suggests that probabilistic generative models can capture how people generate simple types of...

Continuous space word embeddings learned from large, unstructured corpora have been shown to be effective at capturing semantic regularities in language. In this paper we replace LDA’s parameterization of “topics” as categorical distributions over opaque word types with multivariate Gaussian distributions on the embedding space. This encourages the model to group words that are a priori known t...

Abstract Background The substitution of nurses for doctors is a strategy used in primary care to improve access to, and efficiency quality of, care. Many these prescribe medicines including antibiotics. Objectives To identify nurse-independent prescriber (NIP) GP numbers England, the proportions types NIPs antibiotic prescriptions dispensed community, impact COVID-19 on volume, rate dispensed. ...

We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We take a Bayesian approach, generating an appropriate prior via a distribution on partitions that we refer to as the nested Chinese restaurant process. This nonparametric prior allows arbitrarily large branching factors a...

Everyday millions of blogs and micro-blogs are posted on the Internet These posts usually come with useful metadata, such as tags, authors, locations, etc. Much of these data are highly specific or personalized. Tracking the evolution of these data helps us to discover trending topics and users’ interests, which are key factors in recommendation and advertisement placement systems. In this pape...

We study Principal Component Analysis (PCA) in a setting where a part of the corrupting noise is data-dependent and, as a result, the noise and the true data are correlated. Under a bounded-ness assumption on the true data and the noise, and a simple assumption on data-noise correlation, we obtain a nearly optimal sample complexity bound for the most commonly used PCA solution, singular value d...