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Merged
merged 14 commits into from
Jun 3, 2022
Merged

Update home page #26

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e67cd40
Restructure home page. Remove some code to make it more succinct.
twiecki Jun 2, 2022
ff48b02
Add get started quick links.
twiecki Jun 3, 2022
64eb9ee
Replace website repo with link to github.
twiecki Jun 3, 2022
39b2977
Move get started up before announcements.
twiecki Jun 3, 2022
adc3f6b
Rework what sets us apart.
twiecki Jun 3, 2022
171a91d
Minor.
twiecki Jun 3, 2022
1443fdc
Rewording.
twiecki Jun 3, 2022
6a18601
Add sponsors.
twiecki Jun 3, 2022
9540465
Make image a bit less big.
twiecki Jun 3, 2022
4c78d30
Simplify language.
twiecki Jun 3, 2022
24af21f
Fix sponsor grid.
twiecki Jun 3, 2022
0e2f1cd
Fix sponsor grid.
twiecki Jun 3, 2022
a96f91a
Fix center tag
twiecki Jun 3, 2022
1bb2421
Mention bambi.
twiecki Jun 3, 2022
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68 changes: 41 additions & 27 deletions welcome.md
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Expand Up @@ -4,10 +4,21 @@ sd_hide_title: true

# Home

<center><img src="https://github.com/pymc-devs/brand/blob/main/logos/1-pymcdevs.png?raw=true" width="75%"/></center>

{doc}`PyMC <pymc:index>` 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.
PyMC focuses on usability, flexibility, scalability, and extensibility.
Along with core model specification and fitting functionality,
PyMC integrates with {doc}`ArviZ <arviz:index>` for exploratory analysis of the results.

## Features
PyMC strives to make Bayesian modeling as simple and painless as possible, allowing users to focus on their problem rather than the methods.

Here is what sets it apart:

* **Modern**: Includes state-of-the-art inference algorithms, including MCMC (NUTS) and variational inference (ADVI).
* **User friendly**: Write your models using friendly Python syntax. [Learn Bayesian modeling](https://www.pymc.io/projects/docs/en/latest/learn.html#) from the many [example notebooks](https://www.pymc.io/projects/examples/en/latest/gallery.html).
* **Fast**: Uses {doc}`Aesara <aesara:index>` as its computational backend to compile to C and JAX, [run your models on the GPU](https://www.pymc-labs.io/blog-posts/pymc-stan-benchmark/), and benefit from complex graph-optimizations.
* **Batteries included**: Includes probability distributions, Gaussian processes, ABC, SMC and much more. It integrates nicely with {doc}`ArviZ <arviz:index>` for visualizations and diagnostics, as well as {doc}`Bambi <bambi:index>` 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).


## Interactive Demo
```{retrolite} pymc_example.ipynb
Expand All @@ -16,17 +27,12 @@ width: 100%
height: 300px
```

## Features
PyMC strives to make Bayesian modeling as simple and painless as possible,
allowing users to focus on their scientific problem, rather than on the methods used to solve it.
Here is what sets it apart:

* **Modern**: Includes state-of-the-art inference algorithms, including MCMC (NUTS) and variational inference (ADVI).
* **User friendly**: Simple syntax and documentation with many examples and tutorials for building various models.
* **Fast**: Uses {doc}`Aesara <aesara:index>` as its computational backend, which supports compilation to C and JAX, automatic gradient calculation, GPU computing, and complex graph-optimizations.
* **Batteries included**: Comes with a suite of probability distributions, support Gaussian processes, ABC, SMC and much more.
* **Extensible**: Easily incorporates custom step methods and probability distributions.
* **Deployable**: Bayesian models can be embedded in larger programs, and results can be analyzed with the full power of Python.
## 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/howto/api_quickstart.html)
* [Example gallery](https://www.pymc.io/projects/examples/en/latest/gallery.html)
* [Discourse help forum](https://discourse.pymc.io)

## Announcements

Expand Down Expand Up @@ -78,21 +84,29 @@ Sandra Meneses, one of the participants who submitted a PR
::::
:::::

## The PyMC project
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I would keep this, and also add more content on the homepage as proposed here: #14 (comment). Not very opinionated on this being on the homepage, but I think all activities in addition to maintaining pymc should be showcased somewhere around here

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I think the purpose is to show that we're doing more than Just PyMC. But then the activities listed are all very much in support of PyMC. For me it's a bit as if Pandas would say "we also write the Pandas documentation". It's not really surprising and thus has low information density.

And most of these are already on the page elsewhere. Office hours are in the announcements, the example gallery is in the navbar, PyMCon when it's relevant would show up in the announcements.

I do think some of these links would be useful to have here, but I think a user cares less about what we do and rather how he or she can actually accomplish whatever they came to the site for. So what if we replaced this with a: "Get Started" with links to:

  • Installation instructions
  • Beginner User Guide
  • Example Gallery
  • Discourse

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I also added some of these pointers now to the "what sets us apart section" under a new "Community" item.

PyMC is a community driven project with the goal of making Bayesian modeling
and probabilistic programming intuitive and performant.
## Sponsors
:::::{container} full-width
::::{grid} 1 2 2 2
:gutter: 2

:::{grid-item-card} NumFOCUS
:link: https://numfocus.org

<img src="https://www.numfocus.org/wp-content/uploads/2017/03/1457562110.png"/>

NumFOCUS is our non-profit umbrella organization.
:::

:::{grid-item-card} PyMC Labs
:link: https://pymc-labs.io

The flagship of the PyMC project is the PyMC library, but PyMC also coordinates
many other activities:
<img src="https://github.com/pymc-labs/brand/blob/main/logos/4-pymc-labs-transp-black.png?raw=true"/>

* Organizing [PyMCon](https://pymcon.com)
* Curating the {doc}`PyMC example gallery <nb:index>`
* Answer questions and moderate discussions on [PyMC Discourse](https://discourse.pymc.io/)
* Translating the code examples of Bayesian statistics books in
[PyMC resources](https://github.com/pymc-devs/pymc-resources)
* [Office hours](https://discourse.pymc.io/tag/office-hours) and
[sprints](https://pymc-data-umbrella.xyz/en/latest/) to encourage people to contribute to open source
PyMC Labs offers professional consulting services for PyMC.
:::

::::
:::::

:::{toctree}
:hidden:
Expand All @@ -111,5 +125,5 @@ Twitter <https://twitter.com/pymc_devs>
YouTube <https://www.youtube.com/c/PyMCDevelopers>
LinkedIn <https://www.linkedin.com/company/pymc/>
Meetup <https://www.meetup.com/pymc-online-meetup/>
Website repo <https://github.com/pymc-devs/pymc.io>
GitHub <https://www.github.com/pymc-devs/pymc>
:::