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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...
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...
Variational Gaussian Process Dynamical Systems
Gaussian Dynamical Systems Machine Learning
2011/10/9
Abstract: High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphi...
Gaussian Process Techniques for Wireless Communications
Gaussian Process Techniques Wireless Communications
2010/11/9
Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. Classical solutions such that Kalman filter and Particle filter are introduced in this report. Ga...
A Liminf Result on Two-parameter Gaussian Process
fractional wiener process increments liminfs
2007/12/10
In this paper, a liminf behavior is studied of a two-parameter Gaussian process which is a generalization of a two-parameter Wiener process. The results improve on the liminfs in [7].