In this paper, a multi-fidelity multi-output Gaussian process (MMGP) model is proposed to deal with multi-fidelity (MF) data with multiple correlated outputs. It aims to approximate multiple correlated high-fidelity (HF) functions enhanced by the low-fidelity (LF) data. The MMGP model can ...
在本文中我们将以高斯过程回归(Gaussian process regression,GPR)模型为例,对MOGP最基础的几个模型,即Intrinsic coregionalization model (ICM)、Semiparametric Latent Factor Model(SLFM)和Linear model of coregionalization(LMC)分别进行介绍。 注:本文内容主要参考Alvarez MA et al.的综述文章[1]及其slides[2]。
参考: Dreisteine:高斯过程回归(Gaussian Process Regression) 多元正态分布的极大似然估计_Joyliness的博客-CSDN博客_多元正态分布的极大似然估计 Enzo:多输出高斯过程 (multiple output GP) marsggbo:如何理解正定矩阵和半正定矩阵 youtube.com/watch? andrewcharlesjones.github.io ...
A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued random fields in the context of pattern recognition. As a generative model, the multi-output GP is able to handle vector-valued functions with continuous inputs, as ...
Multi-output Gaussian process using a Gaussian kernel and a Gaussian covariance function This example shows how it is possible to make multiple regression over four outputs using a Gaussian process constructed with the convolution process approach. Note that there are some ranges of missing data for...
Multi-Output Gaussian Process Emulator mogp_emulatoris a Python package for fitting Gaussian Process Emulators to computer simulation results. The code contains routines for fitting GP emulators to simulation results with a single or multiple target values, optimizing hyperparameter values, and making pr...
Information about the optimization process, returned as a structure with these fields. output FieldMeaning problemtype Type of problem: 'unconstrained'— No constraints 'boundconstraints'— Only bound constraints 'linearconstraints' — Linear constraints, with or without bound constraints 'nonlinearconstr'...
A complete cycle of the IMM estimator with Kalman filters as its modematched filters is summarized in Table I for the Markovian jump linear system described by (5)–(7), with Gaussian white process and measurement noises. It can be seen that the algorithm is neither involved nor computationall...
The output of the benchmark function 5 (BF5) waving uniformly within the region. The benchmark function 6 (BF6) has a Gaussian function located in the center and another Gaussian function with some periodic terms. Therefore, it produces a single peak region and a boundary region where the ...
The computation in convolution layers has five levels of parallelism: synaptic parallelism, neuron parallelism, input feature map parallelism, output feature map parallelism, and batch processing parallelism. These five parallelization methods make up the whole convolution process. The levels are sorted fro...