swMATH ID: 38824
Software Authors: James. W. Nightingale, Richard G. Hayes, Matthew Griffiths
Description: PyAutoFit: A Classy Probabilistic Programming Language for Model Composition and Fitting. A major trend in academia and data science is the rapid adoption of Bayesian statistics for data analysis and modeling, leading to the development of probabilistic programming languages (PPL). A PPL provides a framework that allows users to easily specify a probabilistic model and perform inference automatically. PyAutoFit is a Python-based PPL which interfaces with all aspects of the modeling (e.g., the model, data, fitting procedure, visualization, results) and therefore provides complete management of every aspect of modeling. This includes composing high-dimensionality models from individual model components, customizing the fitting procedure and performing data augmentation before a model-fit. Advanced features include database tools for analysing large suites of modeling results and exploiting domain-specific knowledge of a problem via non-linear search chaining. Accompanying PyAutoFit is the autofit workspace (see this https URL), which includes example scripts and the HowToFit lecture series which introduces non-experts to model-fitting and provides a guide on how to begin a project using PyAutoFit. Readers can try PyAutoFit right now by going to the introduction Jupyter notebook on Binder (see this https URL) or checkout our readthedocs(see this https URL) for a complete overview of PyAutoFit’s features.
Homepage: https://arxiv.org/abs/2102.04472
Source Code:  https://github.com/Jammy2211/autofit_workspace
Related Software: emcee; PyMultiNest; NumPy; Numba; Matplotlib; dynesty; corner.py; COLOSSUS; Astropy; pyquad; PySwarms; scikit-image; Scikit; SciPy; AutoLens; gravlens; lenstronomy; visilens; PyLops; PyNUFFT
Cited in: 0 Publications