Computing the volume of a polytope in high dimensions is computationally challenging but
has wide applications. Current state-of-the-art algorithms to compute such volumes rely on …

The development of efficient exact and approximate algorithms for probabilistic inference is
a long-standing goal of artificial intelligence research. Whereas substantial progress has …

We propose novel randomized geometric tools to detect low-volatility anomalies in stock
markets; a principal problem in financial economics. Our modeling of the (detection) problem …

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 …

Effective collaboration between developers of Bayesian inference methods and users is key
to advance our quantitative understanding of biosystems. We here present hopsy, a versatile …

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 …

We tackle the problem of efficiently approximating the volume of convex polytopes, when
these are given in three different representations: H-polytopes, which have been studied …

We present dingo, a Python package that supports a variety of methods to sample from the
flux space of metabolic models, based on state-of-the-art random walks and rounding …

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 …

Uniform sampling from the interior of high dimensional polytopes is a very challenging from
a computational point-of-view. Nevertheless, it has a wide range of applications, among …