*pySELFI* is a statistical software package which implements the *Simulator Expansion for Likelihood-Free Inference* (*SELFI*) algorithm.

pySELFI is written in python 3 and licensed under GPLv3. The code is publicly available on GitHub, and the documentation is available on Read the Docs.

Limited user-support may be asked by email.

### The SELFI algorithm

SELFI (Leclercq *et al*. 2019) is part of the family of *approximate Bayesian computation* (ABC) methods, which replace the use of the likelihood function with a data-generating *"black-box" simulator*. It builds upon a novel Gaussian effective likelihood, and upon the linearisation of black-box models around an expansion point. Further assuming that the prior is Gaussian, the effective posterior is Gaussian, with mean and covariance given by two *"filter equations"* reproduced below. The computational workload is fixed *a priori* by the user, and perfectly parallel.

*top*), and a summary of the statistical variables appearing in the equations and their interpretation in the context of galaxy survey data analysis (

*bottom*).

An article on the Aquila Consortium's website discusses SELFI (in particular in comparison to BOLFI). It is available here: Algorithms for likelihood-free cosmological data analysis.

Primordial matter power spectrum inference with pySELFI, using a Gaussian random field data model. The measurements of the final galaxy power spectrum in BOSS (0.2 <*z*< 0.5 bin, SGC, pre-recon, Beutler

*et al.*2016) are overplotted.

### Key features

Current *key features* of pySELFI are:

- implementation of the core "filter equations" of SELFI,
- support of different black-box simulators,
- input and output from simulation pools stored as hdf5 files,
- optimisation of the prior hyperparameters for primordial power spectrum inference.

### Reference

To acknowledge the use of pySELFI in research papers, please cite its doi:10.5281/zenodo.3341588 (or for the latest version, see the badge above), as well as the paper Leclercq *et al*. (2019):

Primordial power spectrum and cosmology from black-box galaxy surveys

F. Leclercq, W. Enzi, J. Jasche, A. Heavens

MNRAS**490**, 4237 (2019), arXiv:1902.10149 [astro-ph.CO] ADS pdf