Zobrazeno 1 - 10
of 599
pro vyhledávání: '"65c40"'
We propose and analyse a novel, fully discrete numerical algorithm for the approximation of the generalised Stokes system forced by transport noise -- a prototype model for non-Newtonian fluids including turbulence. Utilising the Gradient Discretisat
Externí odkaz:
http://arxiv.org/abs/2412.14316
Priors with non-smooth log densities have been widely used in Bayesian inverse problems, particularly in imaging, due to their sparsity inducing properties. To date, the majority of algorithms for handling such densities are based on proximal Langevi
Externí odkaz:
http://arxiv.org/abs/2411.11403
This paper aims at improving the convergence to equilibrium of finite ergodic Markov chains via permutations and projections. First, we prove that a specific mixture of permuted Markov chains arises naturally as a projection under the KL divergence o
Externí odkaz:
http://arxiv.org/abs/2411.08295
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 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 plug & play Langevin samplers.
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