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Modular Jump Gaussian Processes
Data Science in ScienceGaussian processes (GPs) furnish accurate nonlinear predictions with well-calibrated uncertainty. However, the typical GP setup has a built-in stationarity assumption, making it ill-suited for modeling data from processes with sudden changes, or “jumps ...
Anna R. Flowers +4 more
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Integrated Gaussian Processes for Tracking
IEEE Open Journal of Signal ProcessingIn applications such as tracking and localisation, a dynamical model is typically specified for the modelling of an object's motion. An appealing alternative to the traditional parametric Markovian dynamical models is the Gaussian Process (GP ...
Fred Lydeard +2 more
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Pseudo-Marginal Bayesian Inference for Gaussian Processes [PDF]
, 2014 The main challenges that arise when adopting Gaussian Process priors in probabilistic modeling are how to carry out exact Bayesian inference and how to account for uncertainty on model parameters when making model-based predictions on out-of-sample data.
Filippone, Maurizio, Girolami, Mark
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, 2020 We introduce a novel way to combine boosting with Gaussian process and mixed effects models. This allows for relaxing, first, the zero or linearity assumption for the prior mean function in Gaussian process and grouped random effects models in a flexible non-parametric way and, second, the independence assumption made in most boosting algorithms.
openaire +3 more sources
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
, 2019 This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and ...
AS Stordal +17 more
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Gaussian approximation of suprema of empirical processes [PDF]
, 2012 This paper develops a new direct approach to approximating suprema of general empirical processes by a sequence of suprema of Gaussian processes, without taking the route of approximating whole empirical processes in the sup-norm.
Chernozhukov, Victor +2 more
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Temporal Learning in Video Data Using Deep Learning and Gaussian Processes
International Journal of Prognostics and Health Management, 2016 This paper presents an approach for data-driven modeling of hidden, stationary temporal dynamics in sequential images or videos using deep learning and Bayesian non-parametric techniques.
Devesh K. Jha +2 more
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GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language
Journal of Statistical Software, 2022 Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources.
Jamie Fairbrother +4 more
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Gaussian estimates for symmetric simple exclusion processes [PDF]
, 2005 We prove Gaussian tail estimates for the transition probability of $n$ particles evolving as symmetric exclusion processes on $\bb Z^d$, improving results obtained in \cite{l}.
Landim, C.
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Entropy, 2019 In 2000, Kennedy and O’Hagan proposed a model for uncertainty quantification that combines data of several levels of sophistication, fidelity, quality, or accuracy, e.g., a coarse and a fine mesh in finite-element simulations.
Sascha Ranftl +5 more
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