PyVBMC: Efficient Bayesian inference in Python

No Thumbnail Available

Restricted Availability

Date

2023-05-24, 2023-05-24

Persistent identifier of the Data Catalogue metadata

Creator/contributor

Editor

Journal title

Journal volume

Publisher

Publication Type

software
software

Peer Review Status

Repositories

Zenodo

Access rights

Open

ISBN

ISSN

Description

This upload archives the v1.0.1 release of PyVBMC, as prepared for submission to the Journal of Open Source Software. PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for *black-box* computational models. VBMC is an approximate inference method designed for efficient parameter estimation and model assessment when model evaluations are mildly-to-very expensive (e.g., a second or more) and/or noisy.

Link to original dataset

https://doi.org/10.5281/zenodo.7966315

Keyword (yso)

Keyword

Bayesian inference, machine learning, probabilistic modeling, computational statistics

Publication Series

Journal title

Location of the original dataset