Metadata-Version: 2.1
Name: abc-gp-ipm
Version: 0.0.1
Summary: A simple Python package for implenmenting customised ABC-PMC sampling method and GP models in IPMs.
Author: Zhixiao Zhu
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: gpflow ==2.9.0

# ABC GP IPM

The integration of Gaussian Process (GP) models with Approximate Bayesian Computation (ABC) has been explored as a flexible framework for constructing Integral Projection Models (IPMs), enabling non-parametric modelling of demographic relationships and the incorporation of population-level information without explicit likelihoods. However, the practical implementation of this framework - particularly the selection of ABC summary statistics and the execution of ABC-PMC samplers - remains 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.
