Bayesian inference in decomposable graphical models using sequential Monte Carlo methods
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 loglinear 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).
Installation
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 aptget install pythondev graphviz libgraphvizdev pkgconfig
Then run
$ pip install trilearn
It is also possible to pull trilearn as a docker image by
$ docker pull onceltuca/trilearn
Running the tests
$ make test
Usage
See the Jupyter notebooks for examples of usage.
Scripts
Continuous data
To approximate the underlying decomposable graph posterior given the dataset sample_data/data_ar15.csv run
$ pgibbs_ggm_sample N 50 M 1000 f sample_data/data_ar15.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.
Discrete data
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.
Estimate the number of decomposable graphs
To estimate the number of decomposable graphs with up to 15 nodes run for example
$ count_chordal_graphs p 15 N 20000
Built With
Authors
 Felix L. Rios just send me an email in case of any questions, felix.leopoldo.rios at gmail com
References
 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, (to appear) 2022
 T. Pavlenko, F. L. Rios, Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs, ArXiv 2018
License
This project is licensed under the Apache 2.0 License  see the LICENSE file for details
Acknowledgments
 Jim Holmstrom