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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：mcGP: mesh-clustered Gaussian process emulator for partial differential equation systems
mcGP 偏微分方程组 网格聚类 高斯过程 仿真器
2023/4/18
Partial differential equations (PDEs) have become an essential tool for modeling complex physical systems. Such equations are typically solved numerically via mesh-based methods, such as the finite el...
Reforging the Wedding Ring: Exploring a Semi-Artificial Model of Population for the United Kingdom with Gaussian process emulators
agent-based computational demography Gaussian process emulator multistate models population dynamics sensitivity analysis
2014/11/25
Background: We extend the "Wedding Ring‟ agent-based model of marriage formation to include some empirical information on the natural population change for the United Kingdom together with behav...
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...
Optimal Control of dams using P(M,Lambda,tau) policies when the input process is an inverse Gaussian process
Optimal Control of dams P(M,Lambda,tau) policies When the input process inverse Gaussian process
2012/11/23
We consider the P(M,lambda,tau) maintenance policy of a dam using the total discounted and long-run average costs, when the input process is inverse Gaussian.
On Simulations from the Two-Parameter Poisson-Dirichlet Process and the Normalized Inverse-Gaussian Process
Dirichlet process Nonparametric Bayesian inference Normalized inverse-Gaussian process Simulation Stable law process Stick-breaking representation Two-parameter Poisson-Dirichlet process.
2012/11/23
In this paper, we develop simple, yet efficient, procedures for sampling approximations of the two-Parameter Poisson-Dirichlet Process and the normalized inverse-Gaussian process. We compare the effic...
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...