BITS for GAPS

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Bayesian Information-Theoretic Sampling for hierarchical GAussian Process Surrogates.

A framework for information-theoretic sequential experimental design with Bayesian hierarchical Gaussian-process surrogates. Prior physical knowledge is encoded through priors on the GP hyperparameters; sampling is guided by maximizing the predictive differential entropy, so hyperparameter uncertainty (not just predictive variance) drives data acquisition.

Reference: K. D. Jones and A. W. Dowling, "BITS for GAPS: Bayesian Information-Theoretic Sampling for hierarchical GAussian Process Surrogates," Computers & Chemical Engineering 211 (2026) 109650. https://doi.org/10.1016/j.compchemeng.2026.109650

Status: pre-1.0, under active refactor. This repository is being extracted from the paper's research code into a reusable library. See REFACTOR_PLAN.md for the roadmap and HANDOFF.md for the current state.

Install (development)

conda env create -f environment.yml
conda activate bits_for_gaps
pip install -e ".[dev]"          # core + test tools
# pip install -e ".[dev,vle]"    # add the Julia/Clapeyron VLE example backend

The core library is pure Python (GPflow / TensorFlow / NumPy / SciPy) with no Julia dependency. Julia + Clapeyron are only needed for the vle_distillation example.

macOS note: set export PYTHON_JULIACALL_HANDLE_SIGNALS=yes before importing juliacall, or Julia crashes with a bus error (SIGBUS).

Layout

src/bits_for_gaps/   the library (algorithm)
examples/            worked examples (incl. the paper's VLE/distillation case study)
paper/               scripts + reference metrics to reproduce the published figures
tests/               unit / integration / regression tests
docs/                Sphinx documentation (ReadTheDocs)

Quick test

pytest -q

Docs

pip install -e ".[docs]"
sphinx-build -W docs docs/_build/html

Full docs (installation, a pure-Python quickstart, theory notes, the VLE example, and the API reference): docs/index.md, or built and browsed locally as above. See HANDOFF.md for ReadTheDocs setup status.

Project links

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Newer release available (0.2.0)

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Credits

Author: Kyla D. Jones

GitHub Statistics

Data verified by PyPI on Aug 6, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

BSD License (BSD 3-Clause License)

Requires

Python <3.10, >=3.9

Provides Extra

dev docs vle

Tags

bayesian-optimization experimental-design gaussian-process hybrid-modeling information-theory surrogate-modeling

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