Variational Inference for Markov Jump Processes
Abstract
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.
Cite
Text
Opper and Sanguinetti. "Variational Inference for Markov Jump Processes." Neural Information Processing Systems, 2007.Markdown
[Opper and Sanguinetti. "Variational Inference for Markov Jump Processes." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/opper2007neurips-variational/)BibTeX
@inproceedings{opper2007neurips-variational,
title = {{Variational Inference for Markov Jump Processes}},
author = {Opper, Manfred and Sanguinetti, Guido},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {1105-1112},
url = {https://mlanthology.org/neurips/2007/opper2007neurips-variational/}
}