Jupyter environment made for Bayesian inference and graphical modeling

Jupyter environment made for Bayesian inference and graphical modeling.

It is based on Jupyter Notebook Data Science Stack and cdeck3r’s R docker image, provides the ability to perform analyses using R, Python and Julia. Pre-loaded with dozens of common data science packages. ✨

Additionally, it provides the following “standard” packages used for Bayesian inference:

- R
- jags
- rjags
- runjags
- HydeNet
- gRain
- gRim
- bnlearn
- rStan
- parallel
- compute.es

- Python
- PyMC3
- PyStan
- BamBI

- Julia
*pull requests welcome!*

**Note:** Plots of bayesian nets require you to use Google Chrome. It will not work within Firefox.

Spin up the container using the command

```
docker run -it --rm -p 8888:8888 leblancfg/jupyter-bayes:latest
```

For other startup options check out Jupyter Notebook Data Science Stack.

Bayesian inference is still a rapidly-moving field, and the popularity of its package ecosystem is evolving with time — what is cutting-edge now will almost certainly not be within a few years. Contributions are very welcome!

- Package Kruschke’s essentials for Doing Bayesian Data Analysis 2nd edition into the notebook workspace
- Set up semantic versinoing for docker image tags