搜索结果: 1-14 共查到“理论统计学 Gaussian process”相关记录14条 . 查询时间(0.086 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...
Gaussian Process Models and Interpolators for Deterministic Computer Simulators
Computer experiment Matrix inverse approximation Regularization
2010/3/11
For many expensive deterministic computer simulators, the outputs do not have replication
error and the desired metamodel (or emulator) is an interpolator of the observed data. Realizations
of Gauss...
Gaussian Process Structural Equation Models with Latent Variables
Gaussian Process Structural Equation Models Latent Variables
2010/3/11
In a variety of disciplines such as social sciences,
psychology, medicine and economics, the
recorded data are considered to be noisy measurements
of latent variables connected by some
causal stru...
Sequential estimation for the spectral density parameter of a stationary Gaussian process
Sequential estimation for the spectral density parameter a stationary Gaussian process
2009/9/24
Sequential estimation for the spectral density parameter of a stationary Gaussian process。
Estimation of Faraday Rotation Measures of the Near Galactic Sky Using Gaussian Process Models
Markov chain Monte Carlo Gaussian process error mixture model spatial model
2009/9/22
Our primary goal is to obtain a smoothed summary estimate of the
magnetic eld generated in and near to the Milky Way by using Faraday rotation
measures (RMs) Each RM in our data set provides an inte...
Gaussian Process Modelling of Asteroseismic Data
stars oscillations — methods statistical — stars individual Hydrae
2010/3/18
The measured properties of stellar oscillations can provide powerful constraints on
the internal structure and composition of stars. To begin this process, oscillation frequencies
must be extracted ...
Posterior consistency of Gaussian process prior for nonparametric binary regression
Binary regression Gaussian process Karhunen–Loeve expansion maximal inequality posterior consistency
2010/4/26
Consider binary observations whose response probability is an
unknown smooth function of a set of covariates. Suppose that a prior
on the response probability function is induced by a Gaussian proce...