Author: Martin Kilbinger
Year: 2022
Email: martin.killbinger@cea.fr

This is a very brief tutorial about how importance sampling for Bayesian parameter inference. Specifically, it describes the case where an existing sample (e.g. a Monte-Carlo Markov chain) can be importance-sampled to obtain samples under a joint posterior distribution.

For a more general tutorial of Bayesian paremeter inference see the Introduction to MCMC and Bayesian inference linked from CosmoStat Tutorial site.

This tutorial contains a single Jupyter notebook with description and executable code.

In order to run the tutorial notebooks tutees will need to have the following installed:

• Python (require >=3.5)
• NumPy (recommend >=1.16.2)
• Emcee (recommend >=3.1.2)
• Matplotlib (recommend >=3.0.3)
• Jupyter (recommend >=1.0.0)

All of the packages listed above can easily be installed using pip.