# GWKokab documentation [GWKokab][GWKokab] offers a robust suite of probability density function (PDF) models for analyzing the distribution of parameters in compact binary coalescence (CBC) sources, such as neutron star or black hole mergers. Researchers use these models to create synthetic populations of CBC sources, aiding in population inference studies. This helps scientists draw statistical conclusions about the astrophysical population of CBC events based on observed data. The models cover various aspects of CBC sources, including mass distribution, spin, and redshift. Built with [`Distribution`](numpyro.distributions.distribution.Distribution) objects, these models leverage NumPyro's integration with [JAX][JAX] for automatic differentiation and GPU acceleration, ensuring mathematical rigor and computational efficiency for large-scale simulations and analyses. [GWKokab][GWKokab] also incorporates [flowMC][flowMC], a normalizing flow-enhanced sampling package for probabilistic inference, providing a powerful framework for Bayesian parameter estimation from complex, high-dimensional distributions. The package includes a wide range of well-established [`gwkokab.models`](gwkokab.models) frequently cited in the literature. It is continuously updated with new models to keep pace with advancements in the field, and community contributions are encouraged. [GWKokab][GWKokab] supports research in gravitational wave astronomy, offering tools for detailed parameter estimation of single events and large-scale population studies. Its flexibility and extensibility make it invaluable for researchers at all levels. ## Table of Contents ```{toctree} :maxdepth: 2 :caption: Getting started installation FAQs cite examples ``` ```{toctree} :maxdepth: 2 :caption: Further resources dev_docs ``` ```{toctree} :caption: Links :hidden: GitHub Repository Changelog ``` [GWKokab]: https://github.com/kokabsc/gwkokab [JAX]: https://github.com/jax-ml/jax [flowMC]: https://github.com/kazewong/flowMC