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Generation of dynamic Bayesian networks

DW David Wilkins
XT Xinzhao Tong
ML Marcus H. Y. Leung
CM Christopher E. Mason
PL Patrick K. H. Lee
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Dynamic Bayesian networks (DBNs) were constructed to analyse the major routes of dispersal between the different types of sites in this study. Following the method of Lax et al. [13], species relative abundances within each sample were aggregated at the family level and log2-transformed. A total of 472 DBNs were generated, with each network representing one combination of location and taxonomic family. Each candidate network had six nodes, representing the six sites sampled in this study. Bayesian network inference with Java Objects (‘Banjo,’ https://users.cs.duke.edu/~amink/software/banjo/) was used to generate the networks with the following settings: no restrictions on network structure (i.e. no forbidden or mandatory edges); i5 discretisation policy; ‘Greedy’ searcher; ‘AllLocalMoves’ proposer; default evaluator and decider; minimum, maximum and mandatory Markov lags of 1 (i.e. all edges representing a single time-point increment); maximum parent count of 5 (the practical maximum in a network with 6 nodes); default stopping and simulated annealing criteria for a dynamic network.

Copyright and License information: The Author(s) ©2021
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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