搜索结果: 1-15 共查到“统计学 Gaussian Process”相关记录24条 . 查询时间(0.14 秒)
Asymptotic normality of a Sobol index estimator in Gaussian process regression framework
Sensitivity analysis Gaussian process regression asymptotic normality stochas-tic simulators Sobol index
2013/6/14
Stochastic simulators such as Monte-Carlo estimators are widely used in science and engineering to study physical systems through their probabilistic representation. Global sensitivity analysis aims t...
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Parallel Gaussian Process Regression Low-Rank Covariance Matrix Approximations
2013/6/14
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due ...
Parallelizing Gaussian Process Calculations in R
distributed computation kriging linear algebra
2013/6/14
We consider parallel computation for Gaussian process calculations to overcome computational and memory constraints on the size of datasets that can be analyzed. Using a hybrid parallelization approac...
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming
Gaussian Process Genetic Programming Structure Identification
2013/6/14
In this contribution we describe an approach to evolve composite covariance functions for Gaussian processes using genetic programming. A critical aspect of Gaussian processes and similar kernel-based...
A Gaussian Process Emulator Approach for Rapid Contaminant Characterization with an Integrated Multizone-CFD Model
xBayesian Framework Gaussian Process Emulator Multizone Models Integrated Multizone-CFD CONTAM Rapid Source Localization and Characterization
2013/6/14
This paper explores a Gaussian process emulator based approach for rapid Bayesian inference of contaminant source location and characteristics in an indoor environment. In the pre-event detection stag...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
GPfit: An R package for Gaussian Process Model Fitting using a New Optimization Algorithm
Computer experiments, clustering, near-singularity, nugget
2013/6/13
Gaussian process (GP) models are commonly used statistical metamodels for emulating expensive computer simulators. Fitting a GP model can be numerically unstable if any pair of design points in the in...
We consider the problem of detecting the periodic part of a function given the observations of some input/output tuples (xi,yi). As they are known for being powerful tools for dealing with such data, ...
Local Gaussian process approximation for large computer experiments
sequential design sequential updating active learning surrogate model emulator compactly supported covariance local kriging neighborhoods
2013/4/27
We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of local sequential desi...
Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood
Gaussian process multiclass classification multinomial probit approximate inference expectation propagation
2012/9/19
We consider probabilistic multinomial probit classification using Gaussian process (GP) priors. The challenges with the multiclass GP classification are the integration over the non-Gaussian posterior...
Expectation Propagation in Gaussian Process Dynamical Systems
Expectation Propagation Gaussian Process Dynamical Systems
2012/9/19
Rich and complex time-series data, such as those generated from engineer-ing systems, financial markets, videos or neural recordings, are now a common feature of modern data analysis. Explaining the p...
Semi-Blind System Identification in Wireless Relay Networks via Gaussian Process Iterated Conditioning on the Modes Estimation
Relay networks System Identification Gaussian processes Kernel methods
2011/7/7
This paper presents a flexible stochastic model developed for a class of cooperative wireless relay networks, in which the relay processing functionality is not known at the destination. The challenge...
Efficient Gaussian Process Regression for Large Data Sets
Bayesian Compressive Sensing Dimension Reduction
2011/7/6
Gaussian processes (GPs) are widely used in nonparametric regression, classification and spatio-temporal modeling, motivated in part by a rich literature on theoretical properties.
Gaussian Process Regression with a Student-t Likelihood
Gaussian process robust regression Student-t likelihood
2011/7/6
This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model. The challenge with the Student-t model is the analytically intractable i...