Bayesian Regularization and Nonnegative Deconvolution (BRAND) is proposed for estimating time delays of acoustic signals in reverberant environments. Sparsity of the nonnegative f...
We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of t...
This chapter discusses decision making under uncertainty. More specifically, it offers an overview of efficient Bayesian and distribution-free algorithms for making near-optimal se...
Among the many ways to model signals, a recent approach that draws considerable attention is sparse representation modeling. In this model, the signal is assumed to be generated a...
Javier Turek, Irad Yavneh, Matan Protter, Michael ...
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...