State-Space Inference and Learning with Gaussian Processes

By Ryan D. Turner, Marc Peter Deisenroth, Carl E. Rasmussen. 2010

107 citations recorded by the source corpus.

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Citations in the corpus

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction (2019, background): “Non-Parametric State Space Models: Gaussian Process state space models (GP-SSMs) [34], [35] and variational approximations [36], provide an alternative non-parametric approach to forecasting non-linear state space models – mod-”
Gaussian Processes for Data-Efficient Learning in Robotics and Control (2015, neutral): “However, GPDMs typically need a good initialization [53] since the learning problem is very high dimensional.”
Expectation Propagation in Gaussian Process Dynamical Systems (2012, methodology): “More recently, variational [3] and EM-based [20] approaches for learning GPDS were proposed.”
Probabilistic Recurrent State-Space Models (2018, methodology): “Currently, no published and runnable code exists for the model learning frameworks presented in (Turner et al., 2010; Frigola et al., 2013; 2014; Eleftheriadis et al., 2017).”
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC (2013, unknown): “000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053”
Scalable Gradients for Stochastic Differential Equations (2020, background): “This model family generalizes several existing families such as latent ODEs [11, 69] Gaussian state-space models [35, 78], and deep Kalman filters [39], and can naturally handle irregularly-sampled times series and missing observations.”
Learning Scalable Deep Kernels with Recurrent Structure (2016, background): “These models appear to be more general and flexible as they account for uncertainty in the state dynamics, though require complicated approximate training and inference, which are hard to scale (Turner et al., 2010; Frigola et al., 2014).”
Particle gibbs with ancestor sampling (2014, methodology): “An example is the Gaussian process SSM [35, 36], a flexible nonlinear dynamical systems model, for which PGAS has been successfully applied [36].”