BITS for GAPS

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

The paper is bundled in this repository so you can read the method alongside the code: paper/bits_for_gaps_paper.pdf. It is redistributed under CC BY 4.0 (© 2026 The Authors, published by Elsevier Ltd); the DOI above is the canonical citation.

Install

pip install bits_for_gaps

The core library is pure Python (GPflow / TensorFlow / NumPy / SciPy) with no Julia dependency. Julia + Clapeyron are only needed for the vle_distillation example, which isn't part of the PyPI package -- see "From source" below.

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

From source (for examples/, paper/, and development)

git clone https://github.com/dowlinglab/bits_for_gaps
cd bits_for_gaps
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

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

Provenance

The research code behind the paper was originally developed in a private repository over the course of the study. It was then migrated here and reorganized into an installable, tested package: the algorithm was separated from the vapor–liquid-equilibrium case study, generalized to arbitrary input dimension, and covered by a test suite. That private repository holds only the development history — nothing you need to use this package or to reproduce the paper's figures is missing from this repository. The data the figure scripts read is committed here under paper/data/ (see paper/REPRODUCTION.md).

Docs

Full docs (installation, a pure-Python quickstart, theory notes, the VLE example, reproducing the paper's figures, and the API reference): https://bits-for-gaps.readthedocs.io

To build and browse locally instead:

pip install -e ".[docs]"
sphinx-build -W docs docs/_build/html
open docs/_build/html/index.html   # or your platform's equivalent