1. Introduction to PyDelfi

PyDelfi implements Density Estimation Likelihood-Free Inference with neural density estimators and adaptive acquisition of simulations. The implemented methods are described in detail in [Alsing, Charnock, Feeney and Wandelt 2019](https://arxiv.org/abs/1903.00007), and are based closely on [Papamakarios, Sterratt and Murray 2018](https://arxiv.org/pdf/1805.07226.pdf), [Lueckmann et al 2018](https://arxiv.org/abs/1805.09294) and [Alsing, Wandelt and Feeney, 2018](https://academic.oup.com/mnras/article-abstract/477/3/2874/4956055?redirectedFrom=fulltext). Please cite these papers if you use this code!

1.1. Quick start

Once everything is installed, try out either cosmic_shear.ipynb or jla_sne.ipynb as example templates for how to use the code; plugging in your own simulator and letting pydelfi do it’s thing.

If you have a set of pre-run simulations you’d like to throw in rather than allowing pydelfi to run simulations on-the-fly, look at cosmic_shear_prerun_sims.ipynb as a template for how to do this.

If you are interested in using pydelfi with nuisance hardened data compression to project out nuisances ([Alsing & Wandelt 2019](https://arxiv.org/abs/1903.01473v1)), take a look at jla_sne_marginalized.ipynb.