Bayesian predictive classification and structure learning in decomposable graphical models using particle Gibbs.
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 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).
This package currently requires Python 2.7. 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
$ pip install trilearn
It is also possible to pull trilearn as a docker image by
$ docker pull onceltuca/trilearn
Running the tests
$ 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
this will produce a file containing the Markov chain generated by the particle Gibbs algorithm. In order to analyze the chain run
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
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
- 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, ArXiv, 2018
- T. Pavlenko, F. L. Rios, Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs, ArXiv 2018
This project is licensed under the Apache 2.0 License - see the LICENSE file for details
- Jim Holmstrom
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size trilearn-1.1-py2-none-any.whl (75.7 kB)||File type Wheel||Python version py2||Upload date||Hashes View|
|Filename, size trilearn-1.1-py3-none-any.whl (75.7 kB)||File type Wheel||Python version py3||Upload date||Hashes View|