@inproceedings{ba0356b602754eb895ce05c225c099b3,
title = "JUMP-means: Small-variance asymptotics for markov jump processes",
abstract = "Markov jump processes (MJPs) are used to model a wide range of phenomena from disease progression to RNA path folding. However, maximum likelihood estimation of parametric models leads to degenerate trajectories and inferential performance is poor in nonpara-metric models. We take a small-variance asymptotics (SVA) approach to overcome these limitations. We derive the small-variance asymptotics for parametric and nonparametric MJPs for both directly observed and hidden state models. In the parametric case we obtain a novel objective function which leads to non-degenerate trajectories. To derive the nonparametric version we introduce the gamma-gamma process, a novel extension to the gamma-exponential process. We propose algorithms for each of these formulations, which we call JUMP-means. Our experiments demonstrate that JUMP-means is competitive with or outperforms widely used MJP inference approaches in terms of both speed and reconstruction accuracy.",
author = "Huggins, {Jonathan H.} and Karthik Narasimhan and Ardavan Saeedi and Mansinghka, {Vikash K.}",
year = "2015",
language = "English (US)",
series = "32nd International Conference on Machine Learning, ICML 2015",
publisher = "International Machine Learning Society (IMLS)",
pages = "693--701",
editor = "Francis Bach and David Blei",
booktitle = "32nd International Conference on Machine Learning, ICML 2015",
note = "32nd International Conference on Machine Learning, ICML 2015 ; Conference date: 06-07-2015 Through 11-07-2015",
}