We propose the Wasserstein-Fourier (WF) distance to measure the (dis) similarity between
time series by quantifying the displacement of their energy across frequencies. The WF …

Let us consider the deconvolution problem, ie to recover a latent source x (⋅) from the
observations y=[y 1,…, y N] of a convolution process y= x⋆ h+ η, where η is an additive …

We formulate approximate Bayesian inference in non-conjugate temporal and spatio-
temporal Gaussian process models as a simple parameter update rule applied during …

Gaussian processes (GPs) are important probabilistic tools for inference and learning in
spatio-temporal modelling problems such as those in climate science and epidemiology …

Welch's method provides an estimator of the power spectral density that is statistically
consistent. This is achieved by averaging over periodograms calculated from overlapping …

In this article, we focus on the computational efficiency of probabilistic model-based
reinforcement learning (MBRL) in unmanned surface vehicles (USV) under unforeseeable …

Consider a real Gaussian stationary process $ f_\rho $, indexed on either $\mathbb {R} $ or
$\mathbb {Z} $ and admitting a spectral measure $\rho $. We study $\theta_ {\rho}^\ell …

We propose a fully Bayesian approach to wideband, or broadband, direction-of-arrival (DoA)
estimation and signal detection. Unlike previous works in wideband DoA estimation and …

Neonatal seizures are sudden events in brain activity with detrimental effects in neurological
functions usually related to epileptic fits. Though neonatal seizures can be identified from …

In a number of data-driven applications such as detection of arrhythmia, interferometry or
audio compression, observations are acquired indistinctly in the time or frequency domains …