State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction

@article{Hartikainen2012StateSpaceIF,
  title={State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction},
  author={Jouni Hartikainen and Mari Sepp{\"a}nen and Simo S{\"a}rkk{\"a}},
  journal={CoRR},
  year={2012},
  volume={abs/1206.4670}
}
Latent force models (LFMs) are flexible models that combine mechanistic modelling principles (i.e., physical models) with nonparametric data-driven components. Several key applications of LFMs need nonlinearities, which results in analytically intractable inference. In this work we show how non-linear LFMs can be represented as nonlinear white noise driven state-space models and present an efficient non-linear Kalman filtering and smoothing based method for approximate state and parameter… CONTINUE READING
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