About VBLab

VBLab is a probabilistic programming software package, currently available in Matlab, allowing automatic variational Bayesian (VB) inference on many pre-defined common statistical models and also user-defined models.

Key features:

• Providing various Fixed From VB (FFVB) methods and works efficiently for high dimensional and complex posterior distributions.
• Users are not required to know the technicality behind the VB techniques provided; all they need to do is to supply their statistical models, which can be specified flexibly in various ways.

Get started now VBLab on GitHub

Getting started

Install VBLab package

1. Download or clone the VBLab package on VBLab Github Page
2. Add the VBLab package, with all subfolders, to the Matlab search path. See How to add or remove folders to Matlab search path

How to start

1. Read the VB tutorial paper for the theoretical explanation of the VB methods supported by the VBLab package. See also shorter version of the VB tutorial on this site.
2. Run examples showing how to use various VB methods to fit different VBLab and user-defined models. See detail explanation of the examples in the the VB tutorial paper or in the Example section on this site.
3. Check API reference for supported VB techniques, statistical models and how to define custom models for users’ applications.

Authors

• Trong-Nghia Nguyen, PhD candidate, The University of Sydney Business School. (Google scholar, Research gate, LinkedIn)
• Minh-Ngoc Tran, Associate Professor, The University of Sydney Business School. (Google scholar, Research gate, Home Page)
• Viet-Hung Dao, PhD Candidate, The University of New South Wales Business School. (Google scholar, Research gate, Home Page)

Citing VBLab

If you use VBLab in a scientific publication, we would appreciate citations to the following paper:

M.-N. Tran, T.-N. Nguyen and V.-H. Dao (2021). A practical tutorial on Variational Bayes. arXiv2103.01327.

Or bibtex entry :

@misc{tran:2021,
      title={A practical tutorial on Variational Bayes}, 
      author={Minh-Ngoc Tran and Trong-Nghia Nguyen and Viet-Hung Dao},
      year={2021},
      eprint={2103.01327},
      archivePrefix={arXiv},
      primaryClass={stat.CO}
}