IMASL 20939
INSTITUTO DE MATEMATICA APLICADA DE SAN LUIS "PROF. EZIO MARCHI"
Unidad Ejecutora - UE
artículos
Título:
ArviZ a unified library for exploratory analysis of Bayesian models in Python
Autor/es:
RAVIN KUMAR; MARTÍN, OSVALDO ANTONIO; COLIN CARROLL; ARI HARTIKAINEN
Revista:
The Journal of Open Source Software
Editorial:
The Journal of Open Source Software
Referencias:
Año: 2019 vol. 4 p. 1143 - 1147
ISSN:
2475-9066
Resumen:
ArviZ is a Python package for exploratory analysis of Bayesian models. ArviZ aims to be a package that integrates seamlessly with established probabilistic programming languages like PyStan, PyMC, Edward, emcee, Pyro and easily integrated with novel or bespoke Bayesian analyses. Where the aim of the probabilistic programming languages is to make it easy to build and solve Bayesian models, the aim of the ArviZ library is to make it easy to process and analyze the results from the Bayesian models.
