Restructure home page. Remove some code to make it more succinct.

Author: twiecki

welcome.md

@@ -4,30 +4,28 @@ sd_hide_title: true
 
 # Home
 
-{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.
+<img src="https://github.com/pymc-devs/brand/blob/main/logos/1-pymcdevs.png?raw=true"/>
 
-## Interactive Demo
-```{retrolite} pymc_example.ipynb
----
-width: 100%
-height: 300px
-```
+{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.
 
 ## 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.
+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.
+* **Batteries included**: Comes with a suite of probability distributions, support Gaussian processes, ABC, SMC and much more. It integrates nicely with the {doc}`ArviZ <arviz:index>` for visualizaton.
 * **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.
 
+## Interactive Demo
+```{retrolite} pymc_example.ipynb
+---
+width: 100%
+height: 300px
+```
 ## Announcements
 
 :::::{container} full-width
@@ -78,21 +76,6 @@ Sandra Meneses, one of the participants who submitted a PR
 ::::
 :::::
 
-## The PyMC project
-PyMC is a community driven project with the goal of making Bayesian modeling
-and probabilistic programming intuitive and performant.
-
-The flagship of the PyMC project is the PyMC library, but PyMC also coordinates
-many other activities:
-
-* 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
-
 
 :::{toctree}
 :hidden: