Difference between revisions of "GP SSM"

Revision as of 13:50, 13 October 2017

This page gathers references and materials related to the study of "Gaussian Process (GP) State Space Models (SSM)."

Basic Gaussian Process Info

• Rasmussen and Williams

Papers on GP-SSMs

• J.M. Wang, D.J. Fleet, A. Hertzmann, Gaussian Process Dynamical Models
• R. Turner, M.P. Deisenroth, C.E. Rasmussen, State-Space Inference and Learning with Gaussian Process;
• A. McHutchon, Nonlinear Modelling and Control Using Gaussian Processes (Ph.D. thesis, Cambridge University)
• J. Ko, D. Fox, GP-BayesFilters: Bayesian filtering using Gaussian Process Prediction and Observation Models
• F. Perez-Cruz, S.V. Vaerenbergh, J.J. Murrillo-Fuentes, M. Lazarro-Gredilla, and I. Santamaria, Gaussian Processes for Nonlinear Signal Processing;
• A. Svensson, A. Solin, S. Sarkka, T.B. Schon, Computationall Efficient Bayesian Learning of Gaussian Process State Space Models
• A.C. Damianou, M.K. Titsias, N.D. Lawrence, Variational Gaussian Process Dynamical Systems
• M.P. Deisenroth, D. Fox, C.E. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control;
• K. Jocikan, Dynamic GP Models: An Overview and Recent Developments;
• A. Solin, S. Sarkka, Hilbert Space Methods for Reduced-Rank Gaussian Process Regression; (ArXiv.1401.5508)
• C.L.C. Mattos, Z. Dai, A. Damianou, J. Forth, G.A. Barreto, N. Lawrence, Recorruent Gaussian Processes
• N.D. Lawrence, A.J. Moore, Hierarchical Gaussian Process Latent Variable Models

Web Links

• Bibliography on GP Models in Dynamical Systems