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2013 JMLR JMLR 2013

GPstuff: Bayesian Modeling with Gaussian Processes

Abstract

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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2013. (edit, beta)

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning
🧭 Keyword Pioneer — model assessment
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Jarno Vanhatalo , Jaakko Riihimäki , Jouni Hartikainen , Pasi Jylänki , Ville Tolvanen , Aki Vehtari

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Optimization & Theory > Bayesian Inference Deep Learning > Models > Generative Models Machine Learning > Bayesian & Probabilistic > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Gaussian Processes

Keywords

variational inference bayesian inference gaussian process sparse approximation model assessment
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