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Active learning of continuous-time Bayesian networks through interventions*

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Publication:5020035
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DOI10.1088/1742-5468/ac3908OpenAlexW3169241319MaRDI QIDQ5020035

Dominik Linzner, Heinz Koeppl

Publication date: 3 January 2022

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.14742


zbMATH Keywords

statistical inferencemachine learningoptimization under uncertaintynetwork reconstruction


Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)




Cites Work

  • A sufficient condition for pooling data
  • On a Measure of the Information Provided by an Experiment
  • Sequential Optimal Design of Neurophysiology Experiments
  • Markov Chains
  • Follow-Up Designs to Resolve Confounding in Multifactor Experiments
  • Design of follow-up experiments for improving model discrimination and parameter estimation
  • Bayesian Experimental Design: A Review
  • Maximum Entropy Sampling and Optimal Bayesian Experimental Design
  • A Review of Modern Computational Algorithms for Bayesian Optimal Design
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