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Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations

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3 Author(s)
Zammit-Mangion, A. ; Sch. of Inf., Univ. of Edinburgh, Edinburgh, UK ; Sanguinetti, G. ; Kadirkamanathan, V.

Spatiotemporal models are ubiquitous in science and engineering, yet estimation in these models from discrete observations remains computationally challenging. We propose a practical novel approach to inference in spatiotemporal processes, both from continuous and from discrete (point-process) observations. The method is based on a finite-dimensional reduction of the spatiotemporal model, followed by a mean field variational approximate inference approach. To cater for the point-process case, a variational-Laplace approach is proposed which yields tractable computations of approximate variational posteriors. Results show that variational Bayes is a viable and practical alternative to statistical methods such as expectation maximization or Markov chain Monte Carlo.

Published in:

Signal Processing, IEEE Transactions on  (Volume:60 ,  Issue: 7 )