approxposterior

approximate-inference bayesian-inference bayesian-optimization gaussian-processes inference python

Score: 9.798127036878302

Last synced: about 11 hours ago
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Repository metadata:

A Python package for approximate Bayesian inference and optimization using Gaussian processes

Host: GitHub
URL: https://github.com/dflemin3/approxposterior
Owner: dflemin3
License: mit
Created: 2017-11-17T00:03:06.000Z (over 8 years ago)
Default Branch: master
Last Pushed: 2023-11-05T20:37:46.000Z (over 2 years ago)
Last Synced: 2026-06-11T20:03:09.736Z (12 days ago)
Topics: approximate-inference, bayesian-inference, bayesian-optimization, gaussian-processes, inference, python
Language: Python
Homepage: https://dflemin3.github.io/approxposterior/
Size: 50.1 MB
Stars: 43
Watchers: 6
Forks: 10
Open Issues: 5
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- License: LICENSE
- Codemeta: codemeta.json

CodeMeta metadata:

@context: https://raw.githubusercontent.com/codemeta/codemeta/master/codemeta.jsonld
@type: Code
Author:
- - @id: 0000-0001-9293-4043
  - @type: Person
  - Email: dflemin3@uw.edu
  - Name: David Fleming
  - Affiliation: University of Washington
Identifier:
Code repository: github.com/dflemin3/approxposterior
Date published: 2018-04-24
Date modified: 2019-11-27
Date created: 2018-04-24
Description: A python implementation of Kandasamy et al. (2015) for deriving approximate posterior distributions.
Keywords: python, Bayes, posterior, distribution, gaussian process, mcmc
License: MIT
Title: approxposterior
Version: v0.3

Owner metadata:

Name: David Fleming
Login: dflemin3
Email:
Kind: user
Description: Data Science Technical Lead with a PhD in Astronomy from the University of Washington. I predict things slightly better than chance
Website:
Location: St. Louis, MO
Twitter:
Company: Bayer Crop Science
Icon url: https://avatars.githubusercontent.com/u/8882337?u=8f696a47c4fbb122c68b205f4514e04b3658ffa5&v=4
Repositories: 31
Last Synced at: 2024-05-12T00:03:34.716Z
Profile URL: https://github.com/dflemin3

GitHub Events

Total

Fork event: 1
Watch event: 1
Total: 2

Last Year

Total: 0

Committers metadata

Last synced: 4 days ago

Total Commits: 409
Total Committers: 5
Avg Commits per committer: 81.8
Development Distribution Score (DDS): 0.017

Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0

Name	Email	Commits
David Fleming	d**3@u**u	402
Jessica Birky	j**y@u**u	3
Jacob Lustig-Yaeger	j**y@u**u	2
Syrtis Major	s**y@g**m	1
Jed Brown	j**d@j**g	1

Issue and Pull Request metadata

Last synced: 12 days ago

Total issues: 37
Total pull requests: 36
Average time to close issues: 4 months
Average time to close pull requests: 26 days
Total issue authors: 8
Total pull request authors: 5
Average comments per issue: 1.43
Average comments per pull request: 0.03
Merged pull request: 36
Bot issues: 0
Bot pull requests: 0

Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/dflemin3/approxposterior

Top Issue Authors

dflemin3 (28)
jlustigy (3)
RoryBarnes (1)
mgeier (1)
syrte (1)
mrhheffernan (1)
dmdu (1)
tomr-stargazer (1)

Top Pull Request Authors

dflemin3 (32)
jedbrown (1)
jbirky (1)
syrte (1)
jlustigy (1)

Top Issue Labels

enhancement (21)
bug (4)
question (2)

Top Pull Request Labels

enhancement (9)
bugfix (2)

Package metadata

Total packages: 2
Total downloads:
- pypi: 73 last-month
Total dependent packages: 0 (may contain duplicates)
Total dependent repositories: 1 (may contain duplicates)
Total versions: 10
Total maintainers: 1

pypi.org: approxposterior

Gaussian Process Approximation to Posterior Distributions

Homepage: https://github.com/dflemin3/approxposterior
Documentation: https://approxposterior.readthedocs.io/
Licenses: MIT
Latest release: 0.4 (published over 6 years ago)
Last Synced: 2026-06-20T00:04:04.467Z (4 days ago)
Versions: 6
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 73 Last month
Rankings:
- Dependent packages count: 10.105%
- Stargazers count: 10.499%
- Forks count: 11.409%
- Average: 16.036%
- Dependent repos count: 21.593%
- Downloads: 26.576%
Maintainers (1)
- dflemin3

conda-forge.org: approxposterior

This package is a Python implementation of Bayesian Active Learning for Posterior Estimation by Kandasamy et al. (2015) and Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions by Wang & Li (2017). These algorithms allows the user to compute approximate posterior probability distributions using computationally expensive forward models by training a Gaussian Process (GP) surrogate for the likelihood evaluation. The algorithms leverage the inherent uncertainty in the GP's predictions to identify high-likelihood regions in parameter space where the GP is uncertain. The algorithms then run the forward model at these points to compute their likelihood and re-trains the GP to maximize the GP's predictive ability while minimizing the number of forward model evaluations. Check out Bayesian Active Learning for Posterior Estimation by Kandasamy et al. (2015) and Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions by Wang & Li (2017) for in-depth descriptions of the respective algorithms.

Homepage: http://github.com/dflemin3/approxposterior
Licenses: MIT
Latest release: 0.4 (published over 6 years ago)
Last Synced: 2026-04-01T03:21:04.397Z (3 months ago)
Versions: 4
Dependent Packages: 0
Dependent Repositories: 0
Rankings:
- Dependent repos count: 34.025%
- Stargazers count: 40.1%
- Average: 41.868%
- Forks count: 42.171%
- Dependent packages count: 51.175%

Dependencies

setup.py pypi

corner *
emcee *
george *
h5py *
matplotlib *
numpy *
pybind11 *
pytest *
scipy *
sklearn *

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