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.
- Dynamic spatiotemporal modeling
- spatiotemporal point-processes
- stochastic partial differential equations
- variational Bayes
- STATE-SPACE MODELS
- GAUSSIAN COX PROCESSES
- DATA ASSIMILATION