Results 31 to 40 of about 346,567 (317)
Continuity of Gaussian Processes [PDF]
The Annals of Probability, 1986 The author first gives a generalization of \textit{M. B. Marcus} and \textit{L. A. Shepp}'s [Proc. Sixth Berkeley Sympos. math. Statist. Probab., Univ. Calif. 1970, 2, 423-441 (1972; Zbl 0379.60040)] theorem on the equivalence between sample continuity of a Gaussian process defined on a compact subset of a metric space, and a.s.
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Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities [PDF]
, 2009 The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity.
Murray, Iain +6 more
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Journal of Asian Architecture and Building Engineering, 2021 With building energy codes getting strict, quantitative analysis is necessary in the early design stage of high-energy-performance buildings. To fully explore the design space, a highly efficient method is necessary.
Yun Gao +2 more
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J. Mach. Learn. Res., 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.
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Adaptive, cautious, predictive control with Gaussian process priors [PDF]
, 2003 Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon.
Sbarbaro, D. +13 more
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An automation system for vehicle driveability evaluation using machine learning
Nihon Kikai Gakkai ronbunshu, 2022 The drivability is one of the important aspects of vehicle dynamic performances. To ensure quality of the drivability performance, comprehensive screening evaluation is necessary by controlling both complicated driver operation and vehicle behavior ...
Hisashi TAJIMA +4 more
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CoRR, 2012 In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We
Damianou, A.C., Lawrence, N.D.
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Global Optimization Employing Gaussian Process-Based Bayesian Surrogates
Entropy, 2018 The simulation of complex physics models may lead to enormous computer running times. Since the simulations are expensive it is necessary to exploit the computational budget in the best possible manner.
Roland Preuss, Udo von Toussaint
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Convolutional Gaussian Processes
CoRR, 2017 To appear in Advances in Neural Information Processing Systems 30 (NIPS 2017)
van der Wilk, Mark +2 more
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Gaussian Process for Trajectories
, 2023 The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal. This chapter describes Gaussian processes as an interpolation technique for geospatial trajectories.
Kien Nguyen 0003 +2 more
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