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, and are based closely on Papamakarios, Sterratt and Murray 2018, Lueckmann et al 2018 and Alsing, Wandelt and Feeney, 2018. Please cite these papers if you use this code!

Dependencies: tensorflow, getdist, emcee, mpi4py.

Usage: 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), take a look at jla_sne_marginalized.ipynb.

The code is not documented yet (documentation coming imminently), but if you are interested in applying it to your problem please get in touch with us (at [email protected]) - we welcome collaboration!