Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Briol, François-Xavier"'
Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is the large computational cost of simulating data from complex models, an
Externí odkaz:
http://arxiv.org/abs/2410.07930
Autor:
Liu, Xing, Briol, François-Xavier
Goodness-of-fit testing is often criticized for its lack of practical relevance; since ``all models are wrong'', the null hypothesis that the data conform to our model is ultimately always rejected when the sample size is large enough. Despite this,
Externí odkaz:
http://arxiv.org/abs/2408.05854
Publikováno v:
Conference on Uncertainty in Artificial Intelligence (UAI) 2024
We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating integrands is costly. Through the framework of probabilistic numerical methods (such as Bayesian quadrature), our n
Externí odkaz:
http://arxiv.org/abs/2406.16530
Autor:
Duran-Martin, Gerardo, Altamirano, Matias, Shestopaloff, Alexander Y., Sánchez-Betancourt, Leandro, Knoblauch, Jeremias, Jones, Matt, Briol, François-Xavier, Murphy, Kevin
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models. Our method combines generalised Bayesian inference with filtering met
Externí odkaz:
http://arxiv.org/abs/2405.05646
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and simplistic assumption is often violated in practice, which leads to unr
Externí odkaz:
http://arxiv.org/abs/2311.00463
Publikováno v:
PMLR 216:1606-1617, 2023
Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the integrand, and can quantify uncertainty over estimates of an integral. H
Externí odkaz:
http://arxiv.org/abs/2305.13248
Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but constructing effective control variates can be challenging when the number of samples is small. In this paper, we show that when a large number of related i
Externí odkaz:
http://arxiv.org/abs/2303.04756
This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection. The resulting algorithm has key advantages over previous work: it provides provable robustness by leveraging the generalised Bayesian perspectiv
Externí odkaz:
http://arxiv.org/abs/2302.04759
Likelihood-free inference methods typically make use of a distance between simulated and real data. A common example is the maximum mean discrepancy (MMD), which has previously been used for approximate Bayesian computation, minimum distance estimati
Externí odkaz:
http://arxiv.org/abs/2301.11674
Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine; and (2) by changing the overall flow resistance in the farm and thus the so-
Externí odkaz:
http://arxiv.org/abs/2301.01699