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Skew Gaussian processes for classification [PDF]
Machine Learning, 2020 AbstractGaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This implies, for instance, that
Alessio Benavoli +2 more
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Gaussian process deconvolution
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023 Let us consider the deconvolution problem, i.e. to recover a latent sourcex(⋅)from the observationsy=[y1,…,yN]of a convolution processy=x⋆h+η, whereηis an additive noise, the observations inymight have missing parts with respect toy, and the filterhcould be unknown.
Felipe Tobar +2 more
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Applied Sciences, 2019 Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of ...
Zhengmin Kong +3 more
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CoRR, 2011 Appearing in Neural Information Processing Systems ...
Duvenaud, D. +2 more
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Self-tuning control of non-linear systems using gaussian process prior models [PDF]
, 2005 Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the
Sbarbaro, D. +17 more
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CoRR, 2016 Gaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner of additive models and via a link function (like in generalized linear models) to handle non-Gaussian data. However, the link function formalism is restrictive, link functions
Alan D. Saul +3 more
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Gaussian Processes on Hypergraphs
CoRR, 2021 25 pages, 6 ...
Thomas Pinder +3 more
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Online Spatio-Temporal Gaussian Process Experts with Application to Tactile Classification [PDF]
, 2012 16/01/14 meb. conference paper, pre-print version OK to add.In this work, we are primarily concerned with robotic systems that learn online and continuously from multi-variate data-streams.
Soh, Harold +5 more
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Iterative Temporal Learning and Prediction with the Sparse Online Echo State Gaussian Process [PDF]
, 2012 16/01/14 meb. pre-print version OK to add. statement added.In this work, we contribute the online echo state gaussian process (OESGP), a novel Bayesian-based online method that is capable of iteratively learning complex temporal dynamics and producing ...
Soh, Harold +3 more
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Gaussian Process Time-Series Models for Structures under Operational Variability
Frontiers in Built Environment, 2017 A wide range of vibrating structures are characterized by variable structural dynamics resulting from changes in environmental and operational conditions, posing challenges in their identification and associated condition assessment. To tackle this issue,
Luis David Avendaño-Valencia +3 more
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