Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add ArviZ projects to ideas-list.md #450

Merged
merged 2 commits into from
Feb 5, 2024
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion 2024/ideas-list.md
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ page of each organization under the NumFOCUS umbrella at this page.

- [aeon](https://github.com/aeon-toolkit/aeon-admin/blob/main/gsoc/gsoc-2024-projects.md)
- [AiiDA](https://github.com/aiidateam/aiida-core/wiki/GSoC-2024-Projects)
- [ArviZ]
- [ArviZ](https://github.com/arviz-devs/arviz/wiki/GSoC-2024-projects)
- [biocommons](https://github.com/orgs/biocommons/projects/8/views/1)
- [CB-Geo MPM]
- [Colour Science]
Expand Down
4 changes: 2 additions & 2 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -106,10 +106,10 @@ In alphabetic order.
<td>
<h1>ArviZ</h1>
<p>
ArviZ, is a project dedicated to promote and build tools for exploratory analysis of Bayesian models. It currently has a Python and a Julia interface. ArviZ aims to integrate seamlessly with established probabilistic programming languages like PyStan, PyMC (3 and 4), Turing, Soss, emcee or Pyro. Where the aim of the probabilistic programming languages is to make it easy to build and solve Bayesian models, the aim of the ArviZ libraries is to make it easy to process and analyze the results from those Bayesian models.
ArviZ is a project dedicated to promoting and building tools for exploratory analysis of Bayesian models. It currently has a Python and a Julia interface. ArviZ aims to integrate seamlessly with established probabilistic programming languages like PyStan, PyMC, Turing, Soss, emcee, or Pyro. Where the probabilistic programming languages aim to make it easy to build and solve Bayesian models, the ArviZ libraries aim to make it easy to process and analyze the results from those Bayesian models.
</p>
<p>
<a href="https://arviz-devs.github.io/">Website</a> | <a href="https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects">Ideas List</a> | <a href="https://gitter.im/arviz-devs/community"> Contact (Gitter) </a> | <a href="https://github.com/arviz-devs">Source Code</a>
<a href="https://www.arviz.org">Website</a> | <a href="https://github.com/arviz-devs/arviz/wiki/GSoC-2024-projects">Ideas List</a> | <a href="https://gitter.im/arviz-devs/community"> Contact (Gitter) </a> | <a href="https://github.com/arviz-devs">Source Code</a>
</p>
</td>
</tr>
Expand Down
Binary file modified img/arviz.png
100644 → 100755
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.