Zobrazeno 1 - 10
of 237
pro vyhledávání: '"Tempone, Rául"'
In this article we consider likelihood-based estimation of static parameters for a class of partially observed McKean-Vlasov (POMV) diffusion process with discrete-time observations over a fixed time interval. In particular, using the framework of [5
Externí odkaz:
http://arxiv.org/abs/2411.06716
This paper presents an enhanced adaptive random Fourier features (ARFF) training algorithm for shallow neural networks, building upon the work introduced in "Adaptive Random Fourier Features with Metropolis Sampling", Kammonen et al., \emph{Foundatio
Externí odkaz:
http://arxiv.org/abs/2410.06399
This work develops a particle system addressing the approximation of McKean-Vlasov stochastic differential equations (SDEs). The novelty of the approach lies in involving low discrepancy sequences nontrivially in the construction of a particle system
Externí odkaz:
http://arxiv.org/abs/2409.09821
This work addresses stochastic optimal control problems where the unknown state evolves in continuous time while partial, noisy, and possibly controllable measurements are only available in discrete time. We develop a framework for controlling such s
Externí odkaz:
http://arxiv.org/abs/2407.18018
We use the Multi Level Monte Carlo method to estimate uncertainties in a Henry-like salt water intrusion problem with a fracture. The flow is induced by the variation of the density of the fluid phase, which depends on the mass fraction of salt. We a
Externí odkaz:
http://arxiv.org/abs/2404.18003
In this work we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (En
Externí odkaz:
http://arxiv.org/abs/2403.12793
Autor:
Bayer, Christian, Hammouda, Chiheb Ben, Papapantoleon, Antonis, Samet, Michael, Tempone, Raúl
Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. The Monte Carlo (MC) method remains the prevalent choice for pricing engines; however, its slow convergence rate impedes its practical application. Fourier
Externí odkaz:
http://arxiv.org/abs/2403.02832
Autor:
Logashenko, Dmitry, Litvinenko, Alexander, Tempone, Raul, Vasilyeva, Ekaterina, Wittum, Gabriel
Publikováno v:
Journal of Computational Physics, Volume 503, 2024, 112854
We investigate the applicability of the well-known multilevel Monte Carlo (MLMC) method to the class of density-driven flow problems, in particular the problem of salinisation of coastal aquifers. As a test case, we solve the uncertain Henry saltwate
Externí odkaz:
http://arxiv.org/abs/2403.17018
This paper addresses the difficulty of characterizing the time-varying nature of fading channels. The current time-invariant models often fall short of capturing and tracking these dynamic characteristics. To overcome this limitation, we explore usin
Externí odkaz:
http://arxiv.org/abs/2402.09462
We present experimental results highlighting two key differences resulting from the choice of training algorithm for two-layer neural networks. The spectral bias of neural networks is well known, while the spectral bias dependence on the choice of tr
Externí odkaz:
http://arxiv.org/abs/2402.00332