In this paper, we introduce the notion of Gaussian processes indexed by probability density functions for extending Matérn family covariance functions. We use some tools from information geometry to improve efficiency and computational aspects Bayesian learning model. particularly show how a inference with process prior (covariance parameters estimation prediction) can be put into action on spa...

In this paper, we consider the issue of devising a flexible nonlinear function for multichannel blind deconvolution. In particular, we consider the underlying assumption of the source probability density functions. We consider two cases, when the source probability density functions are assumed to be uni-modal, and multimodal respectively. In the unimodal case, there are two approaches: Pearson...