From 6891141dff128be7fffc08d8bc2909acdff65b01 Mon Sep 17 00:00:00 2001 From: aloctavodia Date: Thu, 22 Jan 2026 09:49:34 +0200 Subject: [PATCH 1/2] small tweaks home page --- welcome.md | 46 ++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 38 insertions(+), 8 deletions(-) diff --git a/welcome.md b/welcome.md index 343405a..4e4f25e 100644 --- a/welcome.md +++ b/welcome.md @@ -6,7 +6,7 @@ sd_hide_title: true
-{doc}`PyMC ` is a probabilistic programming library for Python that allows users to build Bayesian models with a simple Python API and fit them using Markov chain Monte Carlo (MCMC) methods. +{doc}`PyMC ` is a probabilistic programming library for Python that allows users to build Bayesian models with a simple Python API and fit them solve them using state of the art algorithms such as Markov chain Monte Carlo (MCMC) methods and variational inference. ## Features PyMC strives to make Bayesian modeling as simple and painless as possible, allowing users to focus on their problem rather than the methods. @@ -19,6 +19,15 @@ Here is what sets it apart: * **Batteries included**: Includes probability distributions, Gaussian processes, ABC, SMC and much more. It integrates nicely with {doc}`ArviZ ` for visualizations and diagnostics, as well as {doc}`Bambi ` for high-level mixed-effect models. * **Community focused**: Ask questions on [discourse](https://discourse.pymc.io), join [MeetUp events](https://meetup.com/pymc-online-meetup/), follow us on [Twitter](https://twitter.com/pymc_devs), and start [contributing](https://www.pymc.io/projects/docs/en/latest/contributing/index.html). +## Get started +* [Installation instructions](https://www.pymc.io/projects/docs/en/latest/installation.html) +* [Beginner guide (if you **do not** know Bayesian modeling)](https://www.pymc.io/projects/docs/en/latest/learn/core_notebooks/pymc_overview.html) +* [API quickstart (if you **do** know Bayesian modeling)](https://www.pymc.io/projects/examples/en/latest/introductory/api_quickstart.html) +* [Example gallery](https://www.pymc.io/projects/examples/en/latest/gallery.html) +* [Discourse help forum](https://discourse.pymc.io) + + + ## Example from Linear Regression This example demonstrates how to perform Bayesian inference for a linear regression model to predict plant growth based on environmental factors. @@ -138,13 +147,34 @@ The new data, under the above scenario would look like: | plant growth[1] | 29.809 | 0.508 | 28.832 | 30.717 | | plant growth[2] | -0.131 | 0.507 | -1.121 | 0.791 | -## Get started -* [Installation instructions](https://www.pymc.io/projects/docs/en/latest/installation.html) -* [Beginner guide (if you **do not** know Bayesian modeling)](https://www.pymc.io/projects/docs/en/latest/learn/core_notebooks/pymc_overview.html) -* [API quickstart (if you **do** know Bayesian modeling)](https://www.pymc.io/projects/examples/en/latest/introductory/api_quickstart.html) -* [Example gallery](https://www.pymc.io/projects/examples/en/latest/gallery.html) -* [Discourse help forum](https://discourse.pymc.io) + +## Cite PyMC + +If you use PyMC in your research, please cite the following paper: + +- [![DOI](https://img.shields.io/badge/DOI-10.7717%2Fpeerj--cs.1516-blue.svg)](https://doi.org/10.7717/peerj-cs.1516) *PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python*, Abril-Pla O, Andreani V, Carroll C, Dong L, Fonnesbeck CJ, Kochurov M, Kumar R, Lao J, Luhmann CC, Martin OA, Osthege M, Vieira R, Wiecki T, Zinkov R. (2023) + + - **BibTeX version** + + ```bibtex + @article{pymc2023, + title = {{PyMC}: A Modern and Comprehensive Probabilistic Programming Framework in {P}ython}, + author = {Oriol Abril-Pla and Virgile Andreani and Colin Carroll and Larry Dong and Christopher J. Fonnesbeck and Maxim Kochurov and Ravin Kumar and Junpeng Lao and Christian C. Luhmann and Osvaldo A. Martin and Michael Osthege and Ricardo Vieira and Thomas Wiecki and Robert Zinkov }, + journal = {{PeerJ} Computer Science}, + volume = {9}, + number = {e1516}, + doi = {10.7717/peerj-cs.1516}, + year = {2023} + } + ``` + +- [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4603970.svg)](https://doi.org/10.5281/zenodo.4603970) A DOI for all versions. + +DOIs for specific versions are shown on [Zenodo](https://zenodo.org) and under [Releases](https://github.com/pymc-devs/pymc/releases). + + + ## Sponsors :::::{container} full-width From 0216e589e2d09fe67fe38f6e7f32a045c4e8daba Mon Sep 17 00:00:00 2001 From: aloctavodia Date: Thu, 22 Jan 2026 10:24:17 +0200 Subject: [PATCH 2/2] fix typo --- welcome.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/welcome.md b/welcome.md index 4e4f25e..aeb021b 100644 --- a/welcome.md +++ b/welcome.md @@ -6,7 +6,7 @@ sd_hide_title: true
-{doc}`PyMC ` is a probabilistic programming library for Python that allows users to build Bayesian models with a simple Python API and fit them solve them using state of the art algorithms such as Markov chain Monte Carlo (MCMC) methods and variational inference. +{doc}`PyMC ` is a probabilistic programming library for Python that allows users to build Bayesian models with a simple Python API and fit them using state of the art algorithms such as Markov chain Monte Carlo (MCMC) methods and variational inference. ## Features PyMC strives to make Bayesian modeling as simple and painless as possible, allowing users to focus on their problem rather than the methods.