Variational inference for Markov jump processes

Markov jump processes play an important role in a large number of application domains. However, realistic systems are analytically intractable and they have traditionally been analysed using simulation based techniques, which do not provide a framework for statistical inference. We propose a mean field approximation to perform posterior inference and parameter estimation. The approximation allows a practical solution to the inference problem, while still retaining a good degree of accuracy. We illustrate our approach on two biologically motivated systems.

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Manfred Opper, Guido Sanguinetti
• Keywords: Information Technology, Mean field Approximation, NIPS 2007, Posterior Inference, Statistical Inference