Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Law, Kody J. H."'
Autor:
Liang, Xinzhu, Lukens, Joseph M., Lohani, Sanjaya, Kirby, Brian T., Searles, Thomas A., Law, Kody J. H.
The Bayesian posterior distribution can only be evaluated up-to a constant of proportionality, which makes simulation and consistent estimation challenging. Classical consistent Bayesian methods such as sequential Monte Carlo (SMC) and Markov chain M
Externí odkaz:
http://arxiv.org/abs/2402.06173
Autor:
Yang, Shangda, Zankin, Vitaly, Balandat, Maximilian, Scherer, Stefan, Carlberg, Kevin, Walton, Neil, Law, Kody J. H.
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations. Often these expectations must be computed by Monte Carlo (MC). The co
Externí odkaz:
http://arxiv.org/abs/2402.02111
We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. Under such an assumption, this work b
Externí odkaz:
http://arxiv.org/abs/2210.15390
Autor:
White, Michael D., Tarakanov, Alexander, Race, Christopher P., Withers, Philip J., Law, Kody J. H.
Finding efficient means of fingerprinting microstructural information is a critical step towards harnessing data-centric machine learning approaches. A statistical framework is systematically developed for compressed characterisation of a population
Externí odkaz:
http://arxiv.org/abs/2203.13718
In this article we consider Bayesian inference associated to deep neural networks (DNNs) and in particular, trace-class neural network (TNN) priors which were proposed by Sell et al. [39]. Such priors were developed as more robust alternatives to cla
Externí odkaz:
http://arxiv.org/abs/2203.12961
We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. This setting is ubiquitous across sci
Externí odkaz:
http://arxiv.org/abs/2203.05351
Autor:
Jasra, Ajay1 (AUTHOR), Law, Kody J. H.2 (AUTHOR) kody.law@manchester.ac.uk, Walton, Neil2 (AUTHOR), Yang, Shangda2 (AUTHOR)
Publikováno v:
Foundations of Computational Mathematics. Aug2024, Vol. 24 Issue 4, p1249-1304. 56p.
This position paper summarizes a recently developed research program focused on inference in the context of data centric science and engineering applications, and forecasts its trajectory forward over the next decade. Often one endeavours in this con
Externí odkaz:
http://arxiv.org/abs/2107.01913
Autor:
Law, Kody J. H., Zankin, Vitaly
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification 10.3 (2022): 1070-1100
This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context of sparsity-promoting priors. In particular, the priors considered arise from scale mixtures of Normal distribut
Externí odkaz:
http://arxiv.org/abs/2102.12261
In this article, we consider computing expectations w.r.t. probability measures which are subject to discretization error. Examples include partially observed diffusion processes or inverse problems, where one may have to discretize time and/or space
Externí odkaz:
http://arxiv.org/abs/2102.12230