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bioRxiv · 10.64898/2026.01.08.698488

ABC_GP_IPM:A Python package for applying GP and ABC to Integral Projection Models, with a Soay sheep case study

Zhu, Z.·Christodoulou, M. D.·Steinsaltz, D.
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Abstract

The integration of Gaussian Process (GP) models with Approximate Bayesian Com-putation (ABC) has been explored as a flexible framework for constructing Integral Projection Models (IPMs), enabling non-parametric modelling of demographic rela-tionships and the incorporation of population-level information without explicit like-lihoods. However, the practical implementation of this framework - particularly the selection of ABC summary statistics and the execution of ABC-PMC samplers - re-mains non-trivial and can limit its broader adoption. To address this gap, we introduce ABC_GP_IPM, a Python package that provides a streamlined and user-friendly interface for constructing GP- and ABC-based IPMs. We demonstrate the utility and performance of the package through a real-world case study of Soay sheep (Ovis aries), illustrating how the software simplifies complex modelling workflows and data management in IPMs.

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Zhu, Z., Christodoulou, M. D., Steinsaltz, D.. 2026-01-09. ABC_GP_IPM:A Python package for applying GP and ABC to Integral Projection Models, with a Soay sheep case study. https://doi.org/10.64898/2026.01.08.698488

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