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.

Authors

• Manfred Opper
• Guido Sanguinetti

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