Gaussian Process State-Space Models with Time-Varying Parameters and Inducing Points

Abstract

We propose time-varying Gaussian process state-space models (TVGPSSM) whose hyper-parameters vary with time. The models have the ability to estimate time-varying functions and thereby increase flexibility to extract information from observed data. The proposed inference approach makes use of time-varying inducing points to adapt to changes of the function, and it exploits hierarchical importance sampling. The experimental results show that the approach has better performance than that of the standard Gaussian process.