10.5281/zenodo.1966609
https://zenodo.org/records/1966609
oai:zenodo.org:1966609
LInzner, Dominik
Dominik
LInzner
Department of Electrical Engineering and Information Technology Technische Universitaet Darmstadt
Koeppl, Heinz
Heinz
Koeppl
Department of Electrical Engineering and Information Technology Department of Biology Technische Universitaet Darmstadt
Cluster Variatonal Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Zenodo
2018
scalability
CTBN dynamics
direct structure learning
2018-12-03
10.5281/zenodo.1966608
https://zenodo.org/communities/precise
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
Continuous-time Bayesian networks (CTBNs) constitute a general and powerful framework for modeling continuous-time stochastic processes on networks. This makes them particularly attractive for learning the directed structures among interacting entities. However, if the available data is incomplete, one needs to simulate the prohibitively complex CTBN dynamics. Existing approximation techniques, such as sampling and low-order variational methods, either scale unfavorably in system size, or are unsatisfactory in terms of accuracy. Inspired by recent advances in statistical physics, we present a new approximation scheme based on cluster-variational methods significantly improving upon existing variational approximations. We can analytically marginalize the parameters of the approximate CTBN, as these are of secondary importance for structure learning. This recovers a
scalable scheme for direct structure learning from incomplete and noisy time-series data. Our approach outperforms existing methods in terms of scalability.
European Commission
10.13039/501100000780
668858
PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION