We present a class of graphical models for directly representing the joint cumulative distribution function (CDF) of many random variables, called cumulative distribution networks...
A strong inductive bias is essential in unsupervised grammar induction. In this paper, we explore a particular sparsity bias in dependency grammars that encourages a small number ...
We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a repr...
This review focuses on dynamic causal analysis of functional magnetic resonance (fMRI) data to infer brain connectivity from a time series analysis and dynamical systems perspecti...
Alard Roebroeck, Anil K. Seth, Pedro A. Valdes-Sos...
A widely agreed upon definition of time series causality inference, established in the seminal 1969 article of Clive Granger (1969), is based on the relative ability of the histor...