mlsanityData to 7 Oct 2026

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

arXiv 1506.02267 ↗First posted 7 Jun 2015stat.CO stat.MLcited by 18

Not on the map. Why?

Abstract (arXiv)

Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure. Learning under this family of models can be conducted using a carefully crafted particle MCMC algorithm. This scheme is computationally efficient and yet allows for a fully Bayesian treatment of the problem. Compared to conventional system identification tools or existing learning methods, we show competitive performance and reliable quantification of uncertainties in the model.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2015.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 0; 2023: 4; 2024: 4; 2025: 5; 2026: 2.

18 papers.

Sort:

  1. 2021Structure-preserving Gaussian Process Dynamicscited by 9
  2. 2023Free-Form Variational Inference for Gaussian Process State-Space Modelscited by 7
  3. 2024Training Bayesian Neural Networks with Sparse Subspace Variational Inferencecited by 7
  4. 2023Sequential Estimation of Gaussian Process-based Deep State-Space Modelscited by 5
  5. 2023Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inferencecited by 5
  6. 2021Probabilistic Robust Linear Quadratic Regulators with Gaussian Processescited by 4
  7. 2021Learning Nonparametric Volterra Kernels with Gaussian Processescited by 4
  8. 2023Out of Distribution Detection via Domain-Informed Gaussian Process State Space Modelscited by 3
  9. 2025Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systemscited by 2
  10. 2024Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurementscited by 1
  11. 2025Canonical Bayesian Linear System Identificationcited by 1
  12. 2025Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledgecited by 1
6 more
  1. 2024Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matricescited by 0
  2. 2024Adaptive Basis Function Selection for Computationally Efficient Predictionscited by 0
  3. 2025pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurementscited by 0
  4. 2025Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process modelscited by 0
  5. 2026A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurementscited by 0
  6. 2026A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regressioncited by 0