Hidden process models

Hidden process models (2006)

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by Rebecca A. Hutchinson , Tom M. Mitchell , Indrayana Rustandi

Venue:	In International Conference of Machine Learning ICML
Citations:	7 - 4 self

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@TECHREPORT{Hutchinson06hiddenprocess,
    author = {Rebecca A. Hutchinson and Tom M. Mitchell and Indrayana Rustandi},
    title = {Hidden process models},
    institution = {In International Conference of Machine Learning ICML},
    year = {2006}
}

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Abstract

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.

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