Gaussian process
Study of the Convergence of the Increments of Gaussian Process
... Wiener process due to [1] are developed to the case of a Gaussian ...to Gaussian processes obtained in ...continuous Gaussian process with X ( ) 0 = 0 , E X t { ( ) } = 0 and { ( ) ( ) ...
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Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation
... a Gaussian process classification model using a logit link function that is based on P´olya-Gamma data augmentation and inducing points for Gaussian process ...
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Spatial Air Quality Prediction Using Gaussian Process
... In this section, we detail the Gaussian Process based inference module that estimates the value of PM2.5 using the data from the monitoring network. First, we model the inference module as a regression ...
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Rainfall Prediction using Gaussian Process Regression Classifier
... Abstract— Forecasting is a procedure of estimating or predicting the future depends on past and nearby data. Forecasting provides information about the impending future measures and their consequences for the ...
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Sparse Gaussian Process Emulators for surrogate design modelling
... a Gaussian process (GP) regression model to simulator evaluations at a small number of predefined inputs in order to learn the simulator’s input to output mapping ...
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Patchwork Kriging for Large-scale Gaussian Process Regression
... the Gaussian process regression problem for large ...auxiliary process that represents the difference between the local GP models on the boundary of the neighboring ...auxiliary process and ...
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Information Rates of Nonparametric Gaussian Process Methods
... We consider the quality of learning a response function by a nonparametric Bayesian approach using a Gaussian process (GP) prior on the response function. We upper bound the quadratic risk of the learning ...
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Generic Inference in Latent Gaussian Process Models
... Our final experiment considered a non-standard inference problem concerning a seismic inversion task. In this problem savigp yielded a solution for the posterior over latent functions that closely matched the solution ...
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Simulation Metamodeling with Gaussian Process: A Numerical Study.
... Mehdad and Kleijnen (2014) proposed a variant of Kriging known as Intrinsic Kriging (IK) which is based on the idea of using integrated random functions as means of filtering trend, equivalent to an integrated ...
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Sparse Additive Gaussian Process with Soft Interactions
... a Gaussian process prior. To induce sparsity within each Gaussian process, we introduce an additional level of soft shrinkage ...additive Gaussian process proposed by Qamar and ...
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Robust Gaussian Process Regression with a Student-t Likelihood
... This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model, which has a non-log-concave likelihood. The challenge with the Student-t model ...
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Ensemble Kalman filtering for online Gaussian process regression and learning
... the Gaussian process estimation provide satisfactory predic- tive accuracy using significantly less computational time in comparison to the GP regression without online ...
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Gaussian Process Models of Sound Change in Indo Aryan Dialectology
... While some aspects of our results were dif- ficult to interpret and remain inconclusive, we did demonstrate a marginal increase in terms of key posterior predictive checks with the use of Gaussian Process ...
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Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning
... The inability to quantify key aspects of creat- ive language is a frequent obstacle to natural language understanding. To address this, we introduce novel tasks for evaluating the cre- ativeness of language—namely, ...
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Posterior consistency of Gaussian process prior for nonparametric binary regression
... the Gaussian measure is shown in Section ...a Gaussian process and its deriva- tives, which is subsequently used to show that a certain function sieve only spares an exponentially small probability ...
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Accelerating multiscale modelling of fluids with on the fly Gaussian process regression
... using Gaussian process regression as a surrogate model for computationally expensive molecular dynamics ...Using Gaussian process regression, we are able to accurately predict atomic-scale ...
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Assessing Approximate Inference for Binary Gaussian Process Classification
... on Gaussian process (GP) priors have attracted much attention in the machine learning ...with Gaussian noise can be done analytically, probabilistic classification using GPs is analytically ...
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Gaussian process operational curves for wind turbine condition monitoring
... As already described above, many papers have used the wind turbine power curve to identify abnormal turbine states. However, many failures associated with underperformance and downtime remain undetected by power curve ...
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Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes
... a Gaussian process regression with constraints on a do- main boundary and also developed a solution approach based on a finite element ...large-scale Gaussian process regression or spatial ...
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A Unifying View of Sparse Approximate Gaussian Process Regression
... dangle individually from the corresponding latent values, by way of the exact (factored) likelihood (5). Left graph: the fully connected graph corresponds to the case where no approximation is made to the full joint ...
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