Zobrazeno 1 - 10
of 620
pro vyhledávání: '"Girolami, Mark A."'
Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology. In these models, a central object of interest is the discrete origin-destination matrix which captures spatial in
Externí odkaz:
http://arxiv.org/abs/2410.07352
In this paper, we develop a class of interacting particle Langevin algorithms to solve inverse problems for partial differential equations (PDEs). In particular, we leverage the statistical finite elements (statFEM) formulation to obtain a finite-dim
Externí odkaz:
http://arxiv.org/abs/2409.07101
Autor:
Glyn-Davies, Alex, Vadeboncoeur, Arnaud, Akyildiz, O. Deniz, Kazlauskaite, Ieva, Girolami, Mark
Variational inference (VI) is a computationally efficient and scalable methodology for approximate Bayesian inference. It strikes a balance between accuracy of uncertainty quantification and practical tractability. It excels at generative modelling a
Externí odkaz:
http://arxiv.org/abs/2409.06560
Autoencoders have found widespread application, in both their original deterministic form and in their variational formulation (VAEs). In scientific applications it is often of interest to consider data that are comprised of functions; the same persp
Externí odkaz:
http://arxiv.org/abs/2408.01362
Bayesian inversion is central to the quantification of uncertainty within problems arising from numerous applications in science and engineering. To formulate the approach, four ingredients are required: a forward model mapping the unknown parameter
Externí odkaz:
http://arxiv.org/abs/2405.17955
Autor:
Bull, Lawrence A., Jeon, Chiho, Girolami, Mark, Duncan, Andrew, Schooling, Jennifer, Haro, Miguel Bravo
We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, representing strain envelopes (of each train passing) for two types of commut
Externí odkaz:
http://arxiv.org/abs/2403.17820
Laplace's method approximates a target density with a Gaussian distribution at its mode. It is computationally efficient and asymptotically exact for Bayesian inference due to the Bernstein-von Mises theorem, but for complex targets and finite-data p
Externí odkaz:
http://arxiv.org/abs/2311.02766
Autor:
Boys, Benjamin, Girolami, Mark, Pidstrigach, Jakiw, Reich, Sebastian, Mosca, Alan, Akyildiz, O. Deniz
Diffusion generative models unlock new possibilities for inverse problems as they allow for the incorporation of strong empirical priors in scientific inference. Recently, diffusion models are repurposed for solving inverse problems using Gaussian ap
Externí odkaz:
http://arxiv.org/abs/2310.06721
Autor:
Hartmann, Marcelo, Williams, Bernardo, Yu, Hanlin, Girolami, Mark, Barp, Alessandro, Klami, Arto
We consider the fundamental task of optimising a real-valued function defined in a potentially high-dimensional Euclidean space, such as the loss function in many machine-learning tasks or the logarithm of the probability distribution in statistical
Externí odkaz:
http://arxiv.org/abs/2308.08305
The abundance of observed data in recent years has increased the number of statistical augmentations to complex models across science and engineering. By augmentation we mean coherent statistical methods that incorporate measurements upon arrival and
Externí odkaz:
http://arxiv.org/abs/2307.05334