State Space representation of non-stationary Gaussian Processes.

Alessio Benavoli, Marco Zaffalon

Type: Preprint

Publication Date: 2016-01-07

Citations: 6

Locations

arXiv (Cornell University) - View

Similar Works

Action	Title	Year	Authors
+	State Space representation of non-stationary Gaussian Processes	2016	Alessio Benavoli Marco Zaffalon
+ PDF Chat	Gaussian Process kernels for popular state-space time series models	2016	Alexander Grigorievskiy Juha Karhunen
+	Gaussian Process Kernels for Popular State-Space Time Series Models	2016	Alexander Grigorievskiy Juha Karhunen
+	Gaussian Process Kernels for Popular State-Space Time Series Models	2016	Alexander Grigorievskiy Juha Karhunen
+	State Space Gaussian Processes with Non-Gaussian Likelihood	2018	Hannes Nickisch Arno Solin Alexander Grigorievskiy
+	State Space Gaussian Processes with Non-Gaussian Likelihood	2018	Hannes Nickisch Arno Solin Alexander Grigorievskiy
+	Identification of Gaussian Process State Space Models	2017	Stefanos Eleftheriadis Thomas F. W. Nicholson Marc Peter Deisenroth James Hensman
+	Identification of Gaussian Process State Space Models	2017	Stefanos Eleftheriadis Thomas F. W. Nicholson Marc Peter Deisenroth James Hensman
+	State-space deep Gaussian processes with applications	2021	Zheng Zhao
+	Linear-time inference for Gaussian Processes on one dimension	2020	Jackson Loper David M. Blei John P. Cunningham Liam Paninski
+	Linear-time inference for Gaussian Processes on one dimension	2020	Jackson Loper David M. Blei John P. Cunningham Liam Paninski
+	A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes	2023	Marcus M. Noack Hengrui Luo Mark D. Risser
+	Towards Flexibility and Interpretability of Gaussian Process State-Space Model	2023	Zhid Lin Feng Yin
+	Unscented Gaussian Process Latent Variable Model: learning from uncertain inputs with intractable kernels.	2019	Daniel Augusto de Souza César Mattos João P. P. Gomes
+	State Space Models	2008	Andrew Harvey
+	State Space Models	2018	Andrew Harvey
+	A NON-GAUSSIAN FAMILY OF STATE-SPACE MODELS WITH EXACT MARGINAL LIKELIHOOD	2013	Dani Gamerman Thiago Rezende dos Santos Glaura C. Franco
+	A flexible state space model for learning nonlinear dynamical systems	2016	Andreas Svensson Thomas B. Schön
+	A flexible state space model for learning nonlinear dynamical systems	2016	Andreas Svensson Thomas B. Schön
+ PDF Chat	A unifying perspective on non-stationary kernels for deeper Gaussian processes	2024	Marcus M. Noack Hengrui Luo Mark D. Risser

Works That Cite This (5)

Action	Title	Year	Authors
+ PDF Chat	Recursive estimation for sparse Gaussian process regression	2020	Manuel Schürch Dario Azzimonti Alessio Benavoli Marco Zaffalon
+	Linear-time inference for Gaussian Processes on one dimension	2020	Jackson Loper David M. Blei John P. Cunningham Liam Paninski
+ PDF Chat	A Gaussian process latent force model for joint input-state estimation in linear structural systems	2019	Rajdip Nayek Souvik Chakraborty Sriram Narasimhan
+	Time series forecasting with Gaussian Processes needs priors	2020	Giorgio Corani Alessio Benavoli Marco Zaffalon
+	Recursive Estimation for Sparse Gaussian Process Regression	2019	Manuel Schürch Dario Azzimonti Alessio Benavoli Marco Zaffalon

Works Cited by This (1)

Action	Title	Year	Authors
+	A New Algorithm for Spline Smoothing Based on Smoothing a Stochastic Process	1987	Robert Kohn Craig F. Ansley