🚀 New to Variational Inference?

Check out A Beginner’s Guide to VI — a hands-on tutorial from @pymc_labs & @PyData Virginia, led by @fonnesbeck

📺 https://www.youtube.com/watch?v=XECLmgnS6Ng

🎥 Learn VI, ADVI, Pathfinder, & real-world modeling tips from Chris.

#Bayesian #PyMC #ML #MCMC

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!

#BayesianStatistics #ScientificPython #PyMC #PyStan #EuroSciPy

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

#BayesianStatistics #ScientificPython #BayesianInference #PyMC #PyStan #EuroSciPy

📢 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 ....

#BayesianMarketing #MMM #CLV #MarketingAnalytics #MachineLearning #ProbabilisticProgramming #DataScience #PyMC #Marketing

Are you a Python enthusiast looking to explore the power of probabilistic programming? Join us next week at PyHouston to hear Larry Jones introduce #PyMC — the go-to Python library for probabilistic modeling and Bayesian inference!

https://www.meetup.com/python-14/events/305485300/?eventOrigin=group_upcoming_events

🔴 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.

#LearningBayesianStatistics #PyMC #forecasting #timeseries

I genuinely miss PyMC2. The #PyMC and #Arviz APIs changes so frequently, that it's impossible to know what the standard approach to anything is.

#Bayesian #Statistics in #Python should be easy.

To be honest, I'd really like a well maintained #SkLearn module for it.

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.

#pydata #pydatalondon #pymc #bayesianstats

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/

#python #DataScience #Bayesian #pymc

@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.

It is possible to fit a graphical model with about 10k variables in #pymc or does it just explode? 🙂

So testing some intuition on moving a deterministic analysis into a complex #Bayesian inference problem using #PyMC has been a really fast process. I'm very impressed with how easy it's been to use it. Well chuffed!!!

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 !

#PyMC #Python #DataScience #Bayesian #OpenSource 🐍📈🎉

Modern #Bayesian samplers like #PyMC are too fast... no way to take a break and having the impression to get things done in parallel... barely the time to toot.

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

#ChangepointModelling #Python #StatisticalModelling

🤔 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

#neuroscience #datascience #statistics #learning

📢 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