HowToFit

Installation Guide | PyAutoFit readthedocs | autofit_workspace

Welcome to HowToFit — the tutorial lecture series for PyAutoFit, an open-source probabilistic programming library for Bayesian model fitting.

HowToFit teaches new users how to compose, fit, and interpret probabilistic models with PyAutoFit. It assumes minimal prior knowledge of statistics and takes you from first principles — what a model is, what a non-linear search does, how a likelihood function is built — through to graphical and hierarchical models for fitting large datasets simultaneously.

For experienced scientists who already know the fundamentals of Bayesian modeling, the autofit_workspace examples will be more appropriate — they are concise, API-focused, and assume the concepts taught in HowToFit as background.

Chapters

• chapter_1_introduction — Models, likelihoods, non-linear searches, why modeling is hard, and how to interpret the results of a fit.
• chapter_2_scientific_workflow — Reserved for future material on building a full scientific model-fitting workflow. Currently a stub; the corresponding overview lives in autofit_workspace/scripts/overview/overview_2_science_workflow.py.
• chapter_3_graphical_models — Fitting many datasets simultaneously with graphical models, hierarchical models, and Expectation Propagation.

Each chapter is organised into numbered tutorial files: chapter_<N>_<name>/tutorial_<M>_<topic>.py (Python script) or the matching .ipynb in notebooks/. Tutorials build on each other within a chapter and assume you have completed the earlier ones.

Getting Started

You can run the tutorials on your own machine by following the PyAutoFit installation guide, then cloning this repository:

git clone https://github.com/PyAutoLabs/HowToFit.git
cd HowToFit

Alternatively, every tutorial can be opened directly in Google Colab via the links in each chapter's README.md.

The tutorials are distributed as both Jupyter notebooks (notebooks/) and Python scripts (scripts/). We recommend the notebooks for reading — figures render inline, and you can step through small code blocks interactively. Use the Python scripts for actual PyAutoFit use.

Before Chapter 1

Before starting chapter 1, open start_here.py for a one-page overview of the series, then begin scripts/chapter_1_introduction/tutorial_1_models.py.

Repository Structure

• scripts/ — Runnable Python tutorial scripts, one subfolder per chapter.
• notebooks/ — Jupyter notebook versions of the scripts (auto-generated; see below).
• config/ — PyAutoFit configuration YAML files used by the tutorials.
• dataset/ — Tutorial 1D datasets are generated at runtime by scripts/simulators/simulators.py — no data files are committed.
• output/ — Model-fit results (generated at runtime, not committed).

Notebooks vs Scripts

Notebooks in notebooks/ are generated from the Python files in scripts/. Always edit the ``.py`` scripts, never the notebooks directly. The # %% markers in each script alternate between code and markdown cells, which PyAutoBuild uses to produce the .ipynb files.

Relationship to autofit_workspace

autofit_workspace is the main user-facing workspace for PyAutoFit — concise examples, cookbooks, and search templates aimed at users who already understand probabilistic modeling. HowToFit is the teaching companion. Tutorials in chapters 1 and 3 reference autofit_workspace scripts as the next place to go after the relevant concept has been introduced.

Citations

If you use HowToFit or PyAutoFit in your research, please cite the references listed in CITATIONS.rst.

Community & Support

Support for PyAutoFit is available via our Slack workspace. Slack is invitation-only; send an email if you'd like an invite.

For installation issues, bug reports, or feature requests, raise an issue on the PyAutoFit GitHub issues page (for library issues) or the HowToFit GitHub issues page (for tutorial content issues).