Zobrazeno 1 - 10
of 40
pro vyhledávání: '"VILANOVA, PEDRO"'
We determine limiting equations for large asymmetric `spin glass' networks. The initial conditions are not assumed to be independent of the disordered connectivity: one of the main motivations for this is that allows one to understand how the structu
Externí odkaz:
http://arxiv.org/abs/2401.15272
Autor:
Vilanova, Pedro
This thesis focuses on the development and analysis of efficient simulation and inference techniques for Markovian pure jump processes with a view towards applications in dense communication networks. These techniques are especially relevant for mode
Externí odkaz:
http://hdl.handle.net/10754/552664
In this study, we demonstrate that the norm test and inner product/orthogonality test presented in \cite{Bol18} are equivalent in terms of the convergence rates associated with Stochastic Gradient Descent (SGD) methods if $\epsilon^2=\theta^2+\nu^2$
Externí odkaz:
http://arxiv.org/abs/2109.10933
In this work we present new scalable, information theory-based variational methods for the efficient model reduction of high-dimensional deterministic and stochastic reaction networks. The proposed methodology combines, (a) information theoretic tool
Externí odkaz:
http://arxiv.org/abs/1807.05319
In this work, we present an extension to the context of Stochastic Reaction Networks (SRNs) of the forward-reverse representation introduced in "Simulation of forward-reverse stochastic representations for conditional diffusions", a 2014 paper by Bay
Externí odkaz:
http://arxiv.org/abs/1504.04155
Publikováno v:
In Journal of Computational Physics 15 January 2020 401
Stochastic modeling of reaction networks is a framework used to describe the time evolution of many natural and artificial systems, including, biochemical reactive systems at the molecular level, viral kinetics, the spread of epidemic diseases, and w
Externí odkaz:
http://arxiv.org/abs/1406.1989
In this work, we extend the hybrid Chernoff tau-leap method to the multilevel Monte Carlo (MLMC) setting. Inspired by the work of Anderson and Higham on the tau-leap MLMC method with uniform time steps, we develop a novel algorithm that is able to co
Externí odkaz:
http://arxiv.org/abs/1403.2943
In this paper we study iterative procedures for stationary equilibria in games with large number of players. Most of learning algorithms for games with continuous action spaces are limited to strict contraction best reply maps in which the Banach-Pic
Externí odkaz:
http://arxiv.org/abs/1210.4657
Akademický článek
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