This library contains Bayesian inference in decomposable (triangulated) graphical models based on sequential Monte Carlo methods. Currently supported functionalities include:

• Bayesian structure learning for discrete log-linear and Gaussian data.

• Estimation of the number of decomopsable graphs with a given number of nodes.

• Predictive classification using Bayesian model averaging (BMA).

• Random generation of junction trees (the Christmas tree algorithm).

If graphviz is not installed, you can install it from brew / aptitude / pacman for example

$ brew install graphviz

On Ubuntu you might need to run

sudo apt-get install python-dev graphviz libgraphviz-dev pkg-config

Then run

$ pip install trilearn

It is also possible to pull trilearn as a docker image by

$ docker pull onceltuca/trilearn

$ make test

See the Jupyter notebooks for examples of usage.

To approximate the underlying decomposable graph posterior given the dataset sample_data/data_ar1-5.csv run

$ pgibbs_ggm_sample -N 50 -M 1000 -f sample_data/data_ar1-5.csv -o results_ggm

this will produce a file containing the Markov chain generated by the particle Gibbs algorithm. In order to analyze the chain run

$ analyze_graph_tajectories -i results_ggm -o results_ggm/plots

this will produce a bunch of files in the current directory to be analyzed.

The data set examples/data/czech_autoworkers.csv contains six binary variables. To generate a particle Gibbs trajectory of decomposable graphs type

$ pgibbs_loglinear_sample -N 50 -M 300 -f sample_data/czech_autoworkers.csv  -o results_loglin

and

$ analyze_graph_tajectories -i results_loglin -o results_loglin/plots

this will produce a number of files in the current directory.

To estimate the number of decomposable graphs with up to 15 nodes run for example

$ count_chordal_graphs -p 15 -N 20000

• NetworkX

• NumPy

• Scipy

• Pandas

• Seaborn

• Felix L. Rios just send me an e-mail in case of any questions, felix.leopoldo.rios at gmail com

• J. Olsson, T. Pavlenko, and F. L. Rios. Bayesian learning of weakly structural Markov graph laws using sequential Monte Carlo methods. Electron. J. Statist., 13(2):2865–2897, 2019.
• J. Olsson, T. Pavlenko, F. L. Rios, Sequential sampling of junction trees for decomposable graphs, Statistics and Computing 32, 80, 2022
• T. Pavlenko, F. L. Rios, Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs, Theor. Probability and Math. Statist. 109: 81-99, 2023

This project is licensed under the Apache 2.0 License - see the LICENSE file for details

• Jim Holmstrom