Skip to content
/ mcabc Public

Model Comparison In Approximate Bayesian Computation

License

Notifications You must be signed in to change notification settings

janfb/mcabc

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

mcabc

Binder

Model Comparison In Approximate Bayesian Computation

mcabc is a Python package for performing model comparison in Approximate Bayesian Computation. It enables you to do Bayesian model comparison between models for which a likelihood function is not available. In contrast to previous approaches that are mainly based on variants of rejection sampling, this is a density estimation method that approximates the posterior over models in parametric form using a mixture-density network.

The implementation is part of my master thesis "Model Comparison in Approximate Bayesian Computation". It is work in progress and tailored to the examples presented in the thesis. If you are interested in applying it to a model comparison problem of yours, please let me know.

Documentation

Installation

Clone the repository. Then, in the repository root folder, run

python setup.py install --user 

to install all required packages. Alternatively, you install it via pip using

pip install git+https://github.com/janfb/mcabc.git --process-dependency-links

To test whether everything worked out fine, run

nosetests tests/

in the repository root folder.

Examples

You find a jupyter notebook in the examples folder presenting the methods on a tractable example problem: Bayesian model comparison between a Poisson model and a negative binomial model. More examples will follow soon.

The examples can be found on binder as well: Binder

Please let me know if you have questions or suggestions.

About

Model Comparison In Approximate Bayesian Computation

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages