Monte Carlo sampling is a powerful toolbox of algorithmic techniques widely used for a
number of applications wherein some noisy quantity, or summary statistic thereof, is sought …

Systems Biology is a fundamental field and paradigm that introduces a new era in Biology.
The crux of its functionality and usefulness relies on metabolic networks that model the …

In this paper, we propose a novel framework for estimating systemic risk measures and risk
allocations based on Markov Chain Monte Carlo (MCMC) methods. We consider a class of …

We study the heavy-dense limit of QCD on the lattice with heavy quarks at high density. The
effective three-dimensional theory has a sign problem which is alleviated by sign …

Gathered under the name of metabolic networks, trophic, biochemical, and urban networks
are here handled as a single field. In the Linear Inverse Modeling framework, these highly …

We study the problem of estimating the volume of convex polytopes, focusing on H-and V-
polytopes, as well as zonotopes. Although a lot of effort is devoted to practical algorithms for …

The present article explores the application of randomized control techniques in empirical
asset pricing and performance evaluation. It introduces geometric random walks, a class of …

Portfolio weights solely based on risk avoid estimation errors from the sample mean, but
they are still affected from the misspecification in the sample covariance matrix. To solve this …

We present algorithmic, complexity, and implementation results on the problem of sampling
points in the interior and the boundary of a spectrahedron, that is the feasible region of a …

The present article explores the application of randomized control techniques in empirical
asset pricing and performance evaluation. It introduces geometric random walks, a class of …