给定视图 ,GP模型有如下的定义: 对于多视图而言,共有 个GP模型,并且每个视图都可能有自己的核函数和似然。总体模型的超参数写成 其中,每个 包含核函数参数和似然参数。 因此,Base multi-view VSGP model如下所示: 仅仅是两个单视图VSGP的集成,中间没有产生任何的联系。 作者的思想是使用一个共享的均值,建立两...
高斯过程回归(Gaussian Process Regression, GPR)是使用高斯过程(Gaussian Process, GP)先验对数据进行回归分析的非参数模型(non-parameteric model) 令随机向量X = [x_1, x_2, ..., x_n]服从多元高斯分布X \sim N(\mu, \Sigma),其中:X_1 = [x_1, ..., x_m]为已经观测变量,X_2 = [x_{m+...
在parameterized model的意义下,target function space可以表示成F={fθ:θ∈Rn},deterministic做推断的方式是解优化问题minθ∑j=1NL(fθ(xj),yj)(L表示距离,如果有prior information则加一项regularization),而stochastic的方式指Bayesian inference:计算概率P(θ|Xj,yj:j=1,…,N},根据它做sampling,用sampling进...
但是,高斯过程在高维空间中效率较低。在编程实验中,常见的 GP 框架包括基于 Pytorch 的 GPyTorch 和 Matlab 工具包 GPML 等。 高斯过程隐变量模型(Gaussian Process Latent Variable Model, GPLVM)是一种无监督的非线性降维方法,它使用高斯过程来学习高维数据的低维表示,是 PCA 的一种泛化。在高斯过程回归的中,我...
This study reports the development of a Gaussian Process (GP) model based on BEMS data. The GP model is a data driven model and requires a few dominant inputs. It provides a quick prediction with a far less computation than the whole building simulation tools (e.g. EnergyPlus). The GP ...
In this paper, we propose a sparse Gaussian process model, EigenGP, based on the Karhunen-Loeve (KL) expansion of a GP prior. We use the Nystrom approximation to obtain data dependent eigenfunctions and select these eigenfunctions by evidence maximization. This selection reduces the number of ...
利用相邻 state-action 的空间相关性来加速学习:通过 Gaussian Process(GP)作为函数逼近器。 主要贡献:两个算法。 model-based MFRL 算法 GP-VI-MFRL,估计转换函数,然后使用 value iteration 计算最优策略。 model-free MFRL 算法 GPQ-MFRL,直接估计最优 Q 值以及随后的最优策略。
When the training data are obtained, usually by the space-filling design, the prior Gaussian process (GP) can be updated using the data. Thus, a posterior GP can be established for prediction. The GPM is a nonlinear interpolation model. Due to the inherent nature of the GPM, the ...
3. Gaussian Process Regression GP regression usually consists of two steps: analyzing data obtained from measurements and predicting data at other positions that have not been measured. 3.1. GP Model Our goal is to model the angle-dependent SMC amplitude 𝜓(𝜙𝑘(𝒑))ψ(ϕk(p)) and ...
That is, if {f(x),x∈ℝd} is a Gaussian process, then E(f(x))=m(x) and Cov[f(x),f(x′)]=E[{f(x)−m(x)}{f(x′)−m(x′)}]=k(x,x′). Now consider the following model. h(x)Tβ+f(x), where f(x)~GP(0,k(x,x′)), that is f(x) are from a ...