Bayesian warped Gaussian processes
M Lázaro-Gredilla - Advances in Neural Information …, 2012 - proceedings.neurips.cc
… We demonstrate the superior performance of Bayesian warped GPs on several real data …
Proceeding in a Bayesian fashion, we place priors on g, f, and εi. We use Gaussian process …
Proceeding in a Bayesian fashion, we place priors on g, f, and εi. We use Gaussian process …
Warped gaussian processes
E Snelson, Z Ghahramani… - Advances in neural …, 2003 - proceedings.neurips.cc
… the Gaussian process (GP) framework for regression by learning a nonlinear transformation
of the GP outputs. This allows for non-Gaussian processes and non-Gaussian … full Bayesian …
of the GP outputs. This allows for non-Gaussian processes and non-Gaussian … full Bayesian …
Compositionally-warped Gaussian processes
… Gaussian data, a GP can be warped by a nonlinear transformation (or warping) as performed
by warped … -demanding alternatives such as Bayesian WGPs and deep GPs. However, the …
by warped … -demanding alternatives such as Bayesian WGPs and deep GPs. However, the …
Bayesian alignments of warped multi-output Gaussian processes
… We have proposed the warped and aligned multi-output Gaussian process (AMO-GP), in …
We extended convolution processes [5] with conditionally independent Gaussian processes on …
We extended convolution processes [5] with conditionally independent Gaussian processes on …
Warped gaussian processes and derivative-based sequential designs for functions with heterogeneous variations
… In this article, we adopt an empirical Bayes viewpoint, ie, that the Bayesian paradigm is used
when seeing f as a random element with a GP prior, but the hyperparameter θ is treated as …
when seeing f as a random element with a GP prior, but the hyperparameter θ is treated as …
A robust approach to warped Gaussian process-constrained optimization
… by warped Gaussian processes, have recently been considered in the Bayesian optimization
setting… uncertain functions modeled by warped Gaussian processes. We analyze convexity …
setting… uncertain functions modeled by warped Gaussian processes. We analyze convexity …
Function factorization using warped Gaussian processes
MN Schmidt - Proceedings of the 26th Annual International …, 2009 - dl.acm.org
… We present a nonparametric Bayesian approach to function factorization where the priors
over the factorizing functions are … Specifically, we choose warped Gaussian process priors. …
over the factorizing functions are … Specifically, we choose warped Gaussian process priors. …
Warped Gaussian processes in remote sensing parameter estimation and causal inference
A Mateo-Sanchis, J Muñoz-Marí… - … and Remote Sensing …, 2018 - ieeexplore.ieee.org
… we focus on the Bayesian nonparametric framework in general, and in Gaussian processes
(GPs) [… WARPED GAUSSIAN PROCESSES This section reviews the theory of GP regression, …
(GPs) [… WARPED GAUSSIAN PROCESSES This section reviews the theory of GP regression, …
[HTML][HTML] Spatial modeling of precipitation based on data-driven warping of Gaussian processes
VD Agou, A Pavlides, DT Hristopulos - Entropy, 2022 - mdpi.com
… warped Gaussian process regression (wGPR) using (i) a synthetic test function contaminated
with non-Gaussian … can then be used for Bayesian regression [22]. Herein we consider …
with non-Gaussian … can then be used for Bayesian regression [22]. Herein we consider …
[HTML][HTML] Spatial warped Gaussian processes: Estimation and efficient field reconstruction
… to create a family of warped spatial Gaussian process models which can … warped Gaussian
processes as well as using the characterising statistical properties of the warped processes …
processes as well as using the characterising statistical properties of the warped processes …
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