Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
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In this study we present an advanced Bayesian framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data that simultaneously employs both spatial and sparse properties The basic building block of our method is the general linear model (GML) that constitute a well-known probabilistic approach for regression By treating regression coefficients as random variables, we can apply an appropriate Gibbs distribution function in order to capture spatial constraints of fMRI time series In the same time, sparse properties are also embedded through a RVM-based sparse prior over coefficients The proposed scheme is described as a maximum a posteriori (MAP) approach, where the known Expectation Maximization (EM) algorithm is applied offering closed form update equations We have demonstrated that our method produces improved performance and enhanced functional activation detection in both simulated data and real applications.