Structured Variational Inference in Unstable Gaussian Process State Space Models

Melchior Silvan
Melchior Silvan
Curi Sebastian
Curi Sebastian
Cited by: 0|Views23

Abstract:

Gaussian processes are expressive, non-parametric statistical models that are well-suited to learn nonlinear dynamical systems. However, large-scale inference in these state space models is a challenging problem. In this paper, we propose CBF-SSM a scalable model that employs a structured variational approximation to maintain temporal c...More

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