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of 596
pro vyhledávání: '"65C40"'
In molecular dynamics, transport coefficients measure the sensitivity of the invariant probability measure of the stochastic dynamics at hand with respect to some perturbation. They are typically computed using either the linear response of nonequili
Externí odkaz:
http://arxiv.org/abs/2410.00212
Autor:
Gottwald, Georg A., Reich, Sebastian
We consider the generative problem of sampling from an unknown distribution for which only a sufficiently large number of training samples are available. In this paper, we build on previous work combining Schr\"odinger bridges and Langevin dynamics.
Externí odkaz:
http://arxiv.org/abs/2409.07968
Autor:
Ruzayqat, Hamza, Knio, Omar
This paper presents a new data assimilation (DA) scheme based on a sequential Markov Chain Monte Carlo (SMCMC) DA technique [Ruzayqat et al. 2024] which is provably convergent and has been recently used for filtering, particularly for high-dimensiona
Externí odkaz:
http://arxiv.org/abs/2409.07111
This work is devoted to inverse problems for elliptic partial differential equations in an Euclidean domain, in which the boundary and/or interior conditions are given merely on some accessible portion of the boundary and/or inside the domain, the go
Externí odkaz:
http://arxiv.org/abs/2409.03686
We introduce a new training algorithm for variety of deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov Chain Monte Carlo sampling procedure to iteratively train network layers, avoiding g
Externí odkaz:
http://arxiv.org/abs/2407.11894
Deep learning algorithms - typically consisting of a class of deep neural networks trained by a stochastic gradient descent (SGD) optimization method - are nowadays the key ingredients in many artificial intelligence (AI) systems and have revolutioni
Externí odkaz:
http://arxiv.org/abs/2407.08100
Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression model that is commonly used in causal inference and beyond. Its strong predictive performance is supported by theoretical guarantees that its posterior distribu
Externí odkaz:
http://arxiv.org/abs/2406.19958
Autor:
Bleher, Johannes, Bleher, Michael
Introducing an algebraic framework for modeling limit order books (LOBs) with tools from physics and stochastic processes, our proposed framework captures the creation and annihilation of orders, order matching, and the time evolution of the LOB stat
Externí odkaz:
http://arxiv.org/abs/2406.04969
We propose a new simple and explicit numerical scheme for time-homogeneous stochastic differential equations. The scheme is based on sampling increments at each time step from a skew-symmetric probability distribution, with the level of skewness dete
Externí odkaz:
http://arxiv.org/abs/2405.14373
Autor:
Schuh, Katharina, Whalley, Peter A.
We study three kinetic Langevin samplers including the Euler discretization, the BU and the UBU splitting scheme. We provide contraction results in $L^1$-Wasserstein distance for non-convex potentials. These results are based on a carefully tailored
Externí odkaz:
http://arxiv.org/abs/2405.09992