GPstuff: Bayesian Modeling with Gaussian Processes

被引：0

作者：

Vanhatalo, Jarno ^{[1
]}

Riihimaki, Jaakko ^{[2
]}

Hartikainen, Jouni ^{[2
]}

Jylanki, Pasi ^{[2
]}

Tolvanen, Ville ^{[2
]}

Vehtari, Aki ^{[2
]}

机构：

[1] Univ Helsinki, Dept Environm Sci, FI-00014 Helsinki, Finland

[2] Aalto Univ, Sch Sci, Dept Biomed Engn & Computat Sci, FI-00076 Aalto, Finland

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2013年 / 14卷

基金：

芬兰科学院;

关键词：

Gaussian process; Bayesian hierarchical model; nonparametric Bayes;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

引用

页码：1175 / 1179

页数：5

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