We develop a new framework for estimating joint probability distributions using tensor product reproducing kernel Hilbert spaces (RKHS). Our framework accommodates a low-dimensional, normalized, and positive model of a Radon-Nikodym derivative, which we es ...
We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The funda ...
Accurately quantifying extreme rainfall is important for the design of hydraulic structures, for flood mapping and zoning and for disaster management. In order to produce maps of estimates of 25-year rainfall return levels in Brazil, we selected 893 shorte ...
Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in density ...
We review the current state of statistical modeling of asymptotically independent data. Our discussion includes necessary and sufficient conditions for asymptotic independence, results on the asymptotic independence of statistics of interest, estimation an ...
We study statistical signatures of composite bosons made of two fermions by extending number states to these quantum particles. Two-particle correlations as well as the dispersion of the probability distribution are analyzed. We show that the particle comp ...
The reliability of new overhead electric and telecommunication lines depends principally on the quality of their support structures. These structures are generally made of wood, metal or concrete. The complexity of a natural substance such as wood requires ...
