首页/数据集/A comparison of mixture density networks and Gaussian processes.

A comparison of mixture density networks and Gaussian processes.

NIAID Data Ecosystem2026-03-11 收录

数据链接：

https://figshare.com/articles/dataset/A_comparison_of_mixture_density_networks_and_Gaussian_processes_/11990421 数据链接链接失效反馈

官方服务：

资源简介：

A comparison of mixture density networks and Gaussian processes.

应用场景：

创建时间：

2020-03-16

高斯过程

低秩近似

Low-rank approximation is a popular strategy to tackle the “big n problem” associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are cruci

DataCite Commons2024-11-13 更新130

Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available

计算机模型

高斯过程

Statistically modeling the output of a stochastic computer model can be difficult to do accurately without a large simulation budget. We alleviate this problem by exploiting readily available determin

Mendeley Data2024-06-29 更新120

Transformation and Additivity in Gaussian Processes

高斯过程

统计建模

We discuss the problem of approximating a deterministic function using Gaussian processes (GPs). The role of transformation in GP modeling is not well understood. We argue that transformation of the r

DataCite Commons2020-10-28 更新80

Efficient Algorithms for Bayesian Nearest-Neighbor Gaussian Processes

贝叶斯推断

高斯过程

We consider alternate formulations of recently proposed hierarchical Nearest Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) for improved convergence, faster computing time, and more rob

DataCite Commons2020-08-28 更新120

ProSpar-GP: Scalable Gaussian Process Modeling with Massive Nonstationary Datasets

高斯过程

非平稳数据处理

Gaussian processes (GPs) are a popular class of Bayesian nonparametric models, but its training can be computationally burdensome for massive training datasets. While there has been notable work on sc

DataCite Commons2025-05-30 更新110

A comparison of mixture density networks and Gaussian processes.

资源简介：

相关数据集