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

Andrew Zammit-Mangion*, Guido Sanguinetti, Visakan Kadirkamanathan

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)3449-3459
Number of pages11
JournalIEEE Transactions on Signal Processing
Volume60
Issue number7
DOIs
Publication statusPublished - Jul 2012

Keywords

  • Dynamic spatiotemporal modeling
  • spatiotemporal point-processes
  • stochastic partial differential equations
  • variational Bayes
  • variational-Laplace
  • STATE-SPACE MODELS
  • GAUSSIAN COX PROCESSES
  • DIFFERENTIAL-EQUATIONS
  • PARAMETER-ESTIMATION
  • DATA ASSIMILATION
  • PREDICTION

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