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pro vyhledávání: '"Altamirano, Matias"'
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
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
Autor:
Altamirano, Matías, Tobar, Felipe
Kernel design for Multi-output Gaussian Processes (MOGP) has received increased attention recently. In particular, the Multi-Output Spectral Mixture kernel (MOSM) arXiv:1709.01298 approach has been praised as a general model in the sense that it exte
Externí odkaz:
http://arxiv.org/abs/2202.09233
Autor:
Altamirano, Matías, Uribe, Pablo, Schlotterbeck, Danner, Jiménez, Abelino, Araya, Roberto, van der Molen Moris, Johan, Caballero, Daniela
Publikováno v:
In Neurocomputing 1 May 2022 484:211-222
Akademický článek
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