Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Learning to Decode Cognitive States from Brain Images
Machine Learning
Bayesian Network Learning with Parameter Constraints
The Journal of Machine Learning Research
Classification in Very High Dimensional Problems with Handfuls of Examples
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A combined expression-interaction model for inferring the temporal activity of transcription factors
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
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We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, high-dimensional, non-Markovian, and often involves prior knowledge of the form "hidden event A occurs n times within the interval [t,t′]." HPMs provide a generalization of the widely used General Linear Model approaches to fMRI analysis, and HPMs can also be viewed as a subclass of Dynamic Bayes Networks.