## Interactive Examples

We are using JupyterHub to provide temporary JupyterLab sessions where you can explore MUQ's capabilities without having to install MUQ on your local machine. Click the button below to try it out!

Note that MUQ is in the midst of a significant refactor into a new "MUQ2" library that is more user friendly and powerful. These JupyterLab sessions are using the new MUQ2 library whereas the examples below are for MUQ1. We are in the process of updating this page.

#### A few other notes about the interactive sessions:

- All of the examples listed below (and more) are available in our interactive MUQ sessions.
- Many of the examples employ Jupyter notebooks which provide a mix of documentation and code. However, it is also possible to run the other examples or test your own MUQ code by creating new files and opening a terminal window.
- The interactive sessions use the current develop branch from our bitbucket site.
- Anything you do in your interactive session will be lost when you close your browser.

## Introductory Examples

#### Introduction to Examples

An introduction describing the format of MUQ's examples.

c++#### Explanation of C++ Examples

An introductory example describing how MUQ uses IPython to construct c++ examples.

## Modelling Examples

## Markov chain Monte Carlo (MCMC) Examples

## Optimization Examples

## Transport Map Examples

#### Basic transport map construction

This example provides an initial overview of how to construct a transport map using samples of a target distribution.

c++#### Multiscale Inference

An illustration of multiscale Bayesian inference using transport maps. Transport maps are used to decouple the coarse and fine scales while MCMC is used to sample the coarse posterior.

This work was supported by the DOE Office of Science through the QUEST SciDAC Institute.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.