PyMC is in Google Summer of Code 2025!
We're excited to be part of #GSoC2025 under @NumFOCUS If you're passionate about #Bayesian stats & #OpenSource, this is your chance to contribute to #PyMC!
Deadline: April 8, 18:00 UTC
Apply now: https://www.pymc.io/blog/blog_gsoc_2025_announcement.html
Advancing probabilistic programming for scientific applications?
#EuroSciPy2025 welcomes original research on Bayesian methods, MCMC algorithms, and statistical modeling in #Python.
Submit your work as tutorials, talks, or posters!
Developing Bayesian inference methods for complex scientific problems?
#EuroSciPy2025 is seeking original work on Hamiltonian Monte Carlo, variational inference, and statistical modeling in #Python.
Submit your innovations: https://pretalx.com/euroscipy-2025/cfp #CfP
Episode 126 is Live!
Listen now 
https://learnbayesstats.com/episode/126-mmm-clv-bayesian-marketing-analytics-will-dean
In this episode with
Alex Andorra, Will Dean from
PyMC-Labs explains how Bayesian methods are reshaping marketing analytics, from MMM to CLV estimation and more ....
𝐇𝐨𝐰 𝐓𝐨 𝐅𝐨𝐜𝐮𝐬 𝐎𝐧 𝐖𝐡𝐚𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐈𝐧 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 https://learnbayesstats.com/episode/124-state-space-models-structural-time-series-jesse-grabowski
𝐈𝐧 𝐭𝐡𝐢𝐬 𝐄𝐩𝐢𝐬𝐨𝐝𝐞, 𝐲𝐨𝐮’𝐥𝐥 𝐥𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐭𝐨 𝐚𝐜𝐡𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 with @pymc
Alex Andorra & Jesse Grabowski talk about state space models, simplifying forecasting, applications etc.
Learning about PyMC makes me want to become a statistician.. super interesting way to think about data, but so much goes into building a good model! So many rabbit holes.
Bayesian modelling is clearly super powerful though and seems to offer some answers to some of the most intractable problems with black-box ML. A reliable model with known and understandable inputs is invaluable for certain use cases.
Cohort Revenue & Retention Analysis with Python 
For those who work with cohort data, I recommend checking Dr.Juan Orduz tutorial for cohort revenue and retention analysis with PyMC 
https://www.pymc-labs.com/blog-posts/cohort-revenue-retention/
@charleemos I have found both the #PyMC tutorials (https://www.pymc.io/projects/docs/en/latest/guides/Gaussian_Processes.html) and the #Stan User's Guide (https://mc-stan.org/docs/stan-users-guide/gaussian-processes.html) on #GaussianProcesses good for getting your hands dirty. Seeing GPs in action and fiddling with hyperparameters was helpful for me to understand the mathematical underpinnings.
Did you know that @pymc has a backend enabling live streaming of MCMCs to a #ClickHouse database?
Check out https://github.com/pymc-devs/mcbackend to learn more about #McBackend, and visit https://github.com/pymc-devs/pymc/wiki/GSoC-2024-projects#Extending-McBackend-related-features if you're interested in doing a Google Summer of Code project on this! #PyMC #GSoC #GSoC2024
Introducing "@as_model" in PyMC-Experimental API!
Key Features:
- Simplifies PyMC modeling
- Better code structure
Details: GitHub PR #268 https://github.com/pymc-devs/pymc-experimental/pull/268
Thanks to Theo Rashid, @ricardoV94, Rob Zinkov, @twiecki and Maxim Kochurov !
At the encouragement of @nkaretnikov I made a blogpost on how to use #pymc in a more functional style!
https://www.zinkov.com/posts/2023-alternative-frontends-pymc/
Exciting update for #DataScience enthusiasts! Abuzar has pre-recorded a detailed walkthrough on Changepoint Modeling with #PyMC. 
Dive in before the live event for a head-start: https://www.youtube.com/watch?v=iwNju1o5yQo
Grab the notebook: https://github.com/abuzarmahmood/pymcon_bayesian_changepoint/blob/72102ad6149b86d586595bf4523f40f66eb20c25/Bayesian_Changepoint_Zoo_neural_data.ipynb
Join us live for deeper insights and a Q&A session! https://www.meetup.com/pymc-online-meetup/events/297203071
How does our brain turn flavors into data? Uncover the science with Dr. Abuzar Mahmood at #PyMCon.
Watch the interview: https://www.youtube.com/watch?v=ySF3X45XRyQ
Get into the nitty-gritty of brain signal analysis using #PyMC. Details & chat: https://discourse.pymc.io/t/13251
Calling all data science enthusiasts and PyMC users!
We're excited to announce the PyMC Docathon on November 17th at 3 PM CET (9 AM ET). This is your chance to contribute to the open-source community and help enhance the PyMC example gallery and documentation.
Save the date: Nov 17, 3pm CET / 14 UTC / 6am PT / 9am ET Sign up here: https://www.meetup.com/pymc-online-meetup/events/297172683/ Join the PyMC Discord Server: https://discord.gg/g9vefGNEMH
Lets meet, collaborate, and network with fellow Bayesian enthusiasts. #pymc
New post (and blog)! Predicting my dog's weight with Bayesian models.
https://www.probablycredible.com/blog/bayesian-model-dog-weight/
The PyMC community team will be holding office hours to provide an outlet for the community to ask questions, get help, discuss, etc. Office hours are open to everyone, and anyone should feel welcome to attend
Date: Wednesday, 1st Nov, 2023 
Time: 19 UTC / 12 pm PT / 3 pm ET 
Where: Online, on Zoom Register (for Zoom link): https://www.meetup.com/pymc-online-meetup/events/296914851/
Office hours will last about an hour, so don't worry if you can't make it at exactly this time! see you there
PyMC 5.8.0 is here, packed with some fantastic updates and improvements. 
New Features:
Causal inference: added the do operator for modeling interventions
Added ICAR distribution
Added JAX implementation for MatrixIsPositiveDefinite Op
New example NB Faster Sampling with JAX and Numba, https://www.pymc.io/projects/examples/en/latest/samplers/fast_sampling_with_jax_and_numba.html
... And many more exciting updates, view the summary of changes here https://github.com/pymc-devs/pymc/releases/tag/v5.8.0
