Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Teckentrup, Aretha"'
The focus of this work is the convergence of non-stationary and deep Gaussian process regression. More precisely, we follow a Bayesian approach to regression or interpolation, where the prior placed on the unknown function $f$ is a non-stationary or
Externí odkaz:
http://arxiv.org/abs/2312.07320
This work is concerned with the use of Gaussian surrogate models for Bayesian inverse problems associated with linear partial differential equations. A particular focus is on the regime where only a small amount of training data is available. In this
Externí odkaz:
http://arxiv.org/abs/2307.08343
We consider the computational efficiency of Monte Carlo (MC) and Multilevel Monte Carlo (MLMC) methods applied to partial differential equations with random coefficients. These arise, for example, in groundwater flow modelling, where a commonly used
Externí odkaz:
http://arxiv.org/abs/2306.13493
Bayesian posterior distributions arising in modern applications, including inverse problems in partial differential equation models in tomography and subsurface flow, are often computationally intractable due to the large computational cost of evalua
Externí odkaz:
http://arxiv.org/abs/2302.04518
Stochastic models of varying complexity have been proposed to describe the dispersion of particles in turbulent flows, from simple Brownian motion to complex temporally and spatially correlated models. A method is needed to compare competing models,
Externí odkaz:
http://arxiv.org/abs/2201.01581
We consider the numerical approximation of $\mathbb{P}[G\in \Omega]$ where the $d$-dimensional random variable $G$ cannot be sampled directly, but there is a hierarchy of increasingly accurate approximations $\{G_\ell\}_{\ell\in\mathbb{N}}$ which can
Externí odkaz:
http://arxiv.org/abs/2107.09148
Publikováno v:
Numerical Mathematics and Advanced Applications ENUMATH (2019)
In certain applications involving the solution of a Bayesian inverse problem, it may not be possible or desirable to evaluate the full posterior, e.g. due to the high computational cost of doing so. This problem motivates the use of approximate poste
Externí odkaz:
http://arxiv.org/abs/1911.05669
Bayesian cubature (BC) is a popular inferential perspective on the cubature of expensive integrands, wherein the integrand is emulated using a stochastic process model. Several approaches have been put forward to encode sequential adaptation (i.e. de
Externí odkaz:
http://arxiv.org/abs/1910.02995
Autor:
Teckentrup, Aretha L
This work is concerned with the convergence of Gaussian process regression. A particular focus is on hierarchical Gaussian process regression, where hyper-parameters appearing in the mean and covariance structure of the Gaussian process emulator are
Externí odkaz:
http://arxiv.org/abs/1909.00232
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