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

2012/06/18 by Hartikainen, Jouni, Seppanen, Mari, Sarkka, Simo · 2 citations
#Data Analysis #Earth and Planetary Astrophysics (astro-ph.EP) #FOS: Computer and information sciences #FOS: Physical sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Statistics and Probability (physics.data-an)

paper · doi:10.48550/arxiv.1206.4670

Abstract

Latent force models (LFMs) are flexible models that combine mechanistic modelling principles (i.e., physical models) with non-parametric data-driven components. Several key applications of LFMs need non-linearities, which results in analytically intractable inference. In this work we show how non-linear LFMs can be represented as non-linear white noise driven state-space models and present an efficient non-linear Kalman filtering and smoothing based method for approximate state and parameter inference. We illustrate the performance of the proposed methodology via two simulated examples, and apply it to a real-world problem of long-term prediction of GPS satellite orbits.

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• Inference in Latent Force Models Using Optimal State Estimation
• Bayesian Nonparametric Dynamical Clustering of Time Series

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