examples/paper/README.md

In gschnabel/nucdataBaynet: Bayesian networks for nuclear data evaluation

Examples given in the paper

The folders contain the examples given in the paper Nuclear data evaluation with Bayesian networks available on arxiv.

The workflow to create the Bayesian networks and do inference in them always follows the same steps:

1. Define a datatable with the information about the nodes including prior estimates and uncertainties

2. Define the mappings, i.e., functional relationships, between the nodes

3. Perform inference in the Bayesian network to obtain posterior estimates, uncertainties and covariances of variables of interest

These steps are split into separate files: Step one is implemented in nodes_definition.R, step two in mappings_definition.R and step three in inference.R.

To run the complete example, one needs to change into the directory of the example, e.g., using the instruction setwd(<example_dir>) and then execute the script prefixed by example_.

Afterwards, the Bayesian network and results can be visualized by running the code in plots.R.

gschnabel/nucdataBaynet documentation built on Feb. 3, 2023, 4:13 a.m.