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 analysis modeling, leading to development probabilistic programming languages (PPL). PPL provides a framework that allows users easily specify model perform inference automatically. PyAutoFit Python-based which interfaces with all aspects modeling (e.g., model, data, fitting procedure, visualization, results) therefore complete management every aspect modeling. This includes composing high-dimensionality models from individual components, customizing procedure performing augmentation before model-fit. Advanced features include database tools analysing large suites results exploiting domain-specific knowledge problem via non-linear search chaining. Accompanying autofit workspace (see https://github.com/Jammy2211/autofit_workspace), example scripts HowToFit lecture series introduces non-experts model-fitting guide on how begin project using PyAutoFit. Readers can try right now by going introduction Jupyter notebook Binder https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD) or checkout our readthedocs(see https://pyautofit.readthedocs.io/en/latest/) overview PyAutoFit's features.