When: July the 7th 2021
Additive Bayesian Networks (ABN) have been developed to disentangle complex relationships of highly correlated datasets as frequently encountered in risk factor analysis studies. ABN is an efficient approach to sort out direct and indirect relationships among variables which is surprisingly common in systemic epidemiology. After the tutorial, you will run the particular steps within an ABN analysis with real-world data. You will be able to contrast this approach with standard regression (linear, logistic, Poisson regression, and multinomial models) used for classical risk factor analysis. Towards the end, we also cover Bayesian Model Averaging in the context of an ABN, which is useful to assess the validity of the learned model and more advanced inference on the network.
|11:15 - 12:00||Brief theoretical introduction on Additive Bayesian modelling||Presentation|
|12:00 - 13:00||Hands-on exercises||Presentation / Hands-on exercises / Zip folder|
|13:00 - 13:20||Advanced ABN modelling||Presentation|
|13:20 - 13:40||Hands-on exercises on advanced features||Hands-on exercises / Zip folder|
|13:40 - 13:45||Wrap-up and discussions|
More resources for abn are available at http://r-bayesian-networks.org/
Note: a previous version of this workshop (co presented with Arianna Comin) has been presented at the SVEPM workshop 2019. The material is available here
During the workshop, some questions were asked on slack channel. We create an FAQ. We hope that it could be usefull and that participants enjoyed the workshop.