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Approximate Inference in Latent Diffusion Processes from Continuous Time Observations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 26 (NIPS 2013)
Number of pages9
Volume26
Publication statusPublished - 2013

Abstract

We propose a novel approximate inference approach for continuous time stochastic dynamical systems observed in both discrete and continuous time with noise. Our expectation-propagation approach generalises the classical Kalman-Bucy smoothing procedure to non-Gaussian observations, enabling continuous-time inference in a variety of models, including spiking neuronal models (state-space models with point process observations) and box likelihood models. Experimental results on real and simulated data demonstrate high distributional accuracy and significant computational savings compared to discrete-time approaches in a neural application.

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