Heart rate variability (HRV) is an indicator of the regulation of the heart, see Task Force (Circulation 93(5):1043-1065, 1996). This study compares the regulation of the heart in two cases of healthy subjects within real life situations: Marathon runners and shift workers. After an update on the state of the art on HRV processing, we specify our probabilistic model: We choose modeling heartbea...

We provide conditions on the statistical model and the prior probability law to derive contraction rates of posterior distributions corresponding to data-dependent priors in an empirical Bayes approach for selecting prior hyper-parameter values. We aim at giving conditions in the same spirit as those in the seminal article of Ghosal and van der Vaart [23]. We then apply the result to specific s...

High frequency (HF) band has both military and civilian uses. It can be used either as a primary or backup communication link. Automatic modulation classification (AMC) is of an utmost importance in this band for the purpose of communications monitoring; e.g., signal intelligence and spectrum management. A widely used method for AMC is based on pattern recognition (PR). Such a method has two ma...

In this work we present a novel approach to transfer knowledge between reinforcement learning tasks with continuous states and actions, where the transition and policy functions are approximated by Gaussian Processes (GPs). The novelty in the proposed approach lies in the idea of transferring qualitative knowledge between tasks, we do so by using the GPs’ hyper-parameters used to represent the ...

This talk will cover the main components of sequential modelbased optimization algorithms. Algorithms of this kind represent the state-of-the-art for expensive black-box optimization problems and are getting increasingly popular for hyper-parameter optimization of machine learning algorithms, especially on larger data sets. The talk will cover the main components of sequential model-based optim...

A method for measuring the density of data sets that contain an unknown number of clusters of unknown sizes is proposed. This method, called Pareto Density Estimation (PDE), uses hyper spheres to estimate data density. The radius of the hyper spheres is derived from information optimal sets. PDE leads to a tool for the visualization of probability density distributions of variables (PDEplot). F...

The computational complexity of ab initio electronic structure methods can be decreased through the so-called density fitting scheme. The density fitting scheme is also known as resolution of identity (RI). Density fitting schemes became a popular approach to approximate the four-centre two-electron integrals which appear in the computation of the Fock matrix in the Hartree-Fock (HF) method. In...

In this paper we propose a transform method to compute the prices and greeks of barrier options driven by a class of Lévy processes. We derive analytical expressions for the Laplace transforms in time of the prices and sensitivities of single barrier options in an exponential Lévy model with hyper-exponential jumps. Inversion of these single Laplace transform yields rapid, accurate results. The...

This paper presents a default model-selection procedure for Gaussian graphical models that involves two new developments. First, we develop an objective version of the hyper-inverse Wishart prior for restricted covariance matrices, called the HIW g-prior, and show how it corresponds to the implied fractional prior for covariance selection using fractional Bayes factors. Second, we apply a class...