Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations

Authors

  • Lukas Studer IBM Research Zurich
  • Loic Paulevé Université Paris-Saclay
  • Christoph Zechner ETH Zurich
  • Matthias Reumann IBM Research Zurich
  • María Rodríguez Martínez IBM Research Zurich
  • Heinz Koeppl Technische Universitaet Darmstadt

DOI:

https://doi.org/10.1609/aaai.v30i1.10294

Abstract

Continuous Time Bayesian Networks (CTBNs) provide a powerful means to model complex network dynamics. How- ever, their inference is computationally demanding — especially if one considers incomplete and noisy time-series data. The latter gives rise to a joint state- and parameter estimation problem, which can only be solved numerically. Yet, finding the exact parameterization of the CTBN has often only secondary importance in practical scenarios. We therefore focus on the structure learning problem and present a way to analytically marginalize the Markov chain underlying the CTBN model with respect its parameters. Since the resulting stochastic process is parameter-free, its inference reduces to an optimal filtering problem. We solve the latter using an efficient parallel implementation of a sequential Monte Carlo scheme. Our framework enables CTBN inference to be applied to incomplete noisy time-series data frequently found in molecular biology and other disciplines.

Downloads

Published

2016-03-02

How to Cite

Studer, L., Paulevé, L., Zechner, C., Reumann, M., Rodríguez Martínez, M., & Koeppl, H. (2016). Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10294

Issue

Section

Technical Papers: Machine Learning Methods