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Description:

refgsd 0.2.3

GSD
Installation
| Documentation
| Cite us
Reference implementation of generalised score distribution in python
This library provides a reference implementation of gsd probabilities for correctness and efficient implementation of samples and log_probabilities in jax.
Citations
Theoretical derivation of GSD is described in the following papers.
@Article{Cmiel2023,
author={{\'{C}}miel, Bogdan
and Nawa{\l}a, Jakub
and Janowski, Lucjan
and Rusek, Krzysztof},
title={Generalised score distribution: underdispersed continuation of the beta-binomial distribution},
journal={Statistical Papers},
year={2023},
month={Feb},
day={09},
issn={1613-9798},
doi={10.1007/s00362-023-01398-0},
url={https://doi.org/10.1007/s00362-023-01398-0}
}

@ARTICLE{gsdnawala,
author={Nawała, Jakub and Janowski, Lucjan and Ćmiel, Bogdan and Rusek, Krzysztof and Pérez, Pablo},
journal={IEEE Transactions on Multimedia},
title={Generalized Score Distribution: A Two-Parameter Discrete Distribution Accurately Describing Responses From Quality of Experience Subjective Experiments},
year={2022},
volume={},
number={},
pages={1-15},
doi={10.1109/TMM.2022.3205444}
}

If you decide to apply the concepts presented or base on the provided code, please do refer our related paper.
Installation
You can install gsd via pip:
pip install ref_gsd

Note that you install ref_gsd but import gsd e.g.
import gsd

gsd.fit_moments([2, 8, 2, 0, 0.])

Development
To develop and modify gsd, you need to install
hatch, a tool for Python packaging and
dependency management.
To enter a virtual environment for testing or debugging, you can run:
hatch shell

Running tests
Gsd uses unitest for testing. To run the tests, use the following command:
hatch run test

Standalone estimator
You can quickly estimate GSD parameters from a command line interface
python3 -m gsd -c 1 2 3 4 5

psi=3.6667 rho=0.6000

Acknowledgments
Development of this software has received funding from the Norwegian Financial Mechanism 2014-2021 under project-
2019/34/H/ST6/00599.