A Full Bayesian Approach to Sparse Network Inference Using Heterogeneous Datasets

A Full Bayesian Approach to Sparse Network Inference Using Heterogeneous Datasets

Accepted version

Peer-reviewed

Repository URI

https://www.repository.cam.ac.uk/handle/1810/326015

Repository DOI

https://doi.org/10.17863/CAM.73472

Files

Accepted version (570.85 KB)

Type

Article

Authors

Jin, J

https://orcid.org/0000-0002-7336-6682

Yuan, Y

https://orcid.org/0000-0001-7858-0437

Goncalves, J

https://orcid.org/0000-0002-5228-6165

Keywords

Kernel, Network topology, Topology, Transfer functions, Bayes methods, Monte Carlo methods, Linear systems, Dynamical structure function (DSF), network inference, reversible jump Markov chain Monte Carlo (RJMCMC), sparse networks, system identification

Journal Title

IEEE Transactions on Automatic Control

Journal ISSN

0018-9286
1558-2523

Volume Title

66

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publisher DOI

https://doi.org/10.1109/TAC.2020.3016964

Rights

All rights reserved

Collections

Cambridge University Research Outputs