Learning Graphical Models From the Glauber Dynamics

DOI10.1109/TIT.2017.2713828zbMath1395.62254arXiv1410.7659OpenAlexW2622935962WikidataQ105584788 ScholiaQ105584788MaRDI QIDQ5375563

Guy Bresler, Devavrat Shah, David Gamarnik

Publication date: 14 September 2018

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1410.7659

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of graph theory (05C90) Markov processes: estimation; hidden Markov models (62M05) Interacting random processes; statistical mechanics type models; percolation theory (60K35) Applications of graph theory to circuits and networks (94C15) Graphical methods in statistics (62A09)

Related Items (3)

Estimating the interaction graph of stochastic neuronal dynamics by observing only pairs of neurons ⋮ Structure recovery for partially observed discrete Markov random fields on graphs under not necessarily positive distributions ⋮ Exact recovery in the Ising blockmodel

This page was built for publication: Learning Graphical Models From the Glauber Dynamics