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pro vyhledávání: '"Vaes, U."'
Autor:
Vaes, U
The aim of this note is to revisit propagation of chaos for a Langevin-type interacting particle system used for sampling probability measures. The interacting particle system we consider coincides, in the setting of a log-quadratic target distributi
Externí odkaz:
http://arxiv.org/abs/2404.06456
Estimating the statistics of the state of a dynamical system, from partial and noisy observations, is both mathematically challenging and finds wide application. Furthermore, the applications are of great societal importance, including problems such
Externí odkaz:
http://arxiv.org/abs/2402.01593
Calculating averages with respect to multimodal probability distributions is often necessary in applications. Markov chain Monte Carlo (MCMC) methods to this end, which are based on time averages along a realization of a Markov process ergodic with r
Externí odkaz:
http://arxiv.org/abs/2307.11744
The ensemble Kalman filter is widely used in applications because, for high dimensional filtering problems, it has a robustness that is not shared for example by the particle filter; in particular it does not suffer from weight collapse. However, the
Externí odkaz:
http://arxiv.org/abs/2212.13239
The scaling of the mobility of two-dimensional Langevin dynamics in a periodic potential as the friction vanishes is not well understood for non-separable potentials. Theoretical results are lacking, and numerical calculation of the mobility in the u
Externí odkaz:
http://arxiv.org/abs/2206.09781
We introduce a practical method for incorporating equality and inequality constraints in global optimization methods based on stochastic interacting particle systems, specifically consensus-based optimization (CBO) and ensemble Kalman inversion (EKI)
Externí odkaz:
http://arxiv.org/abs/2111.02970
We propose a novel method for sampling and optimization tasks based on a stochastic interacting particle system. We explain how this method can be used for the following two goals: (i) generating approximate samples from a given target distribution;
Externí odkaz:
http://arxiv.org/abs/2106.02519
Inverse problems are ubiquitous because they formalize the integration of data with mathematical models. In many scientific applications the forward model is expensive to evaluate, and adjoint computations are difficult to employ; in this setting der
Externí odkaz:
http://arxiv.org/abs/2102.00540
In this paper, we study the diffusive limit of solutions to the generalized Langevin equation (GLE) in a periodic potential. Under the assumption of quasi-Markovianity, we obtain sharp longtime equilibration estimates for the GLE using techniques fro
Externí odkaz:
http://arxiv.org/abs/2007.16087
Autor:
Carrillo, J. A., Vaes, U.
We study the convergence to equilibrium of the mean field PDE associated with the derivative-free methodologies for solving inverse problems. We show stability estimates in the euclidean Wasserstein distance for the mean field PDE by using optimal tr
Externí odkaz:
http://arxiv.org/abs/1910.07555