Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
This work addresses computational efficiency in Bayesian learning for state space models, which is incremental as it builds on prior methods with specific improvements.
The authors tackled the problem of computationally efficient Bayesian learning in Gaussian process state space models by projecting onto approximate eigenfunctions and using a particle MCMC algorithm, achieving competitive performance and reliable uncertainty quantification compared to existing methods.
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.