This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Background: Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mappi...
Inspectable student models focus on the idea of letting students and teachers interact with the representation of the student that the system maintains. Both humans and the system ...
We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, a...
Tapani Raiko, Harri Valpola, Markus Harva, Juha Ka...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...