{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 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 from the many example notebooks.
• Fast: Uses {doc}Aesara <aesara:index> as its computational backend to compile to C and JAX, run your models on the GPU, 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, join MeetUp events, follow us on Twitter, and start contributing.

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• Installation instructions
• Beginner guide (if you do not know Bayesian modeling)
• API quickstart (if you do know Bayesian modeling)
• Example gallery
• Discourse help forum

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:::{grid-item-card} PyMC 4.0 is officially released! :link: v4_announcement :link-type: ref :class-header: bg-pymc-three :columns: 12

Release announcement ^^^ PyMC 4.0 is a major rewrite of the library with many great new features while keeping the same modeling API of PyMC3. :::

:::{grid-item-card} PyMC - Office Hours :link: https://discourse.pymc.io/tag/office-hours :class-header: bg-pymc-one

Event ^^^ The PyMC team has recently started hosting office hours regularly. Subscribe on Discourse to be notified of the next event! :::

:::{grid-item-card} Probabilistic Programming in PyMC :link: https://austinrochford.com/posts/intro-prob-prog-pymc.html :class-header: bg-pymc-two

Talk ^^^ Austin Rochford gave the coolest talk on Probabilistic Programming in PyMC 4.0 :::

:::{grid-item-card} Sprint testimonials :link: sprint_testimonial :link-type: ref :class-header: bg-pymc-one

Blog post ^^^ Read about the recent PyMC-Data Umbrella sprint in this interview with Sandra Meneses, one of the participants who submitted a PR :::

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

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

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

PyMC Labs offers professional consulting services for PyMC. :::

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about/ecosystem about/history about/testimonials :::

:::{toctree} :hidden: :caption: External links

Discourse https://discourse.pymc.io 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/ GitHub https://www.github.com/pymc-devs/pymc :::