PyMC developers<p>PyMC is in Google Summer of Code 2025!</p><p>We're excited to be part of <a href="https://bayes.club/tags/GSoC2025" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GSoC2025</span></a> under <span class="h-card" translate="no"><a href="https://mastodon.social/@NumFOCUS" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>NumFOCUS</span></a></span> If you're passionate about <a href="https://bayes.club/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> stats & <a href="https://bayes.club/tags/OpenSource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OpenSource</span></a>, this is your chance to contribute to <a href="https://bayes.club/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a>!</p><p>📅 Deadline: April 8, 18:00 UTC<br>🔗 Apply now: <a href="https://www.pymc.io/blog/blog_gsoc_2025_announcement.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">pymc.io/blog/blog_gsoc_2025_an</span><span class="invisible">nouncement.html</span></a></p>
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EuroSciPy<p>Advancing probabilistic programming for scientific applications?</p><p><a href="https://fosstodon.org/tags/EuroSciPy2025" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>EuroSciPy2025</span></a> welcomes original research on Bayesian methods, MCMC algorithms, and statistical modeling in <a href="https://fosstodon.org/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a>.</p><p>Submit your work as tutorials, talks, or posters!</p><p><a href="https://fosstodon.org/tags/BayesianStatistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BayesianStatistics</span></a> <a href="https://fosstodon.org/tags/ScientificPython" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ScientificPython</span></a> <a href="https://fosstodon.org/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> <a href="https://fosstodon.org/tags/PyStan" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyStan</span></a> <a href="https://fosstodon.org/tags/EuroSciPy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>EuroSciPy</span></a></p>
EuroSciPy<p>Developing Bayesian inference methods for complex scientific problems?</p><p><a href="https://fosstodon.org/tags/EuroSciPy2025" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>EuroSciPy2025</span></a> is seeking original work on Hamiltonian Monte Carlo, variational inference, and statistical modeling in <a href="https://fosstodon.org/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a>.</p><p>Submit your innovations: <a href="https://pretalx.com/euroscipy-2025/cfp" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">pretalx.com/euroscipy-2025/cfp</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/CfP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CfP</span></a></p><p><a href="https://fosstodon.org/tags/BayesianStatistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BayesianStatistics</span></a> <a href="https://fosstodon.org/tags/ScientificPython" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ScientificPython</span></a> <a href="https://fosstodon.org/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BayesianInference</span></a> <a href="https://fosstodon.org/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> <a href="https://fosstodon.org/tags/PyStan" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyStan</span></a> <a href="https://fosstodon.org/tags/EuroSciPy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>EuroSciPy</span></a></p>
Pierre-Simon Laplace<p>📢 Episode 126 is Live! </p><p>🎧 Listen now 👉 <a href="https://learnbayesstats.com/episode/126-mmm-clv-bayesian-marketing-analytics-will-dean" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">learnbayesstats.com/episode/12</span><span class="invisible">6-mmm-clv-bayesian-marketing-analytics-will-dean</span></a></p><p>🎙️ In this episode with <br> Alex Andorra, Will Dean from <br>PyMC-Labs explains how Bayesian methods are reshaping marketing analytics, from MMM to CLV estimation and more ....</p><p><a href="https://mstdn.science/tags/BayesianMarketing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BayesianMarketing</span></a> <a href="https://mstdn.science/tags/MMM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MMM</span></a> <a href="https://mstdn.science/tags/CLV" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CLV</span></a> <a href="https://mstdn.science/tags/MarketingAnalytics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MarketingAnalytics</span></a> <a href="https://mstdn.science/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> <a href="https://mstdn.science/tags/ProbabilisticProgramming" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ProbabilisticProgramming</span></a> <a href="https://mstdn.science/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> <a href="https://mstdn.science/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> <a href="https://mstdn.science/tags/Marketing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Marketing</span></a></p>
Pierre-Simon Laplace<p>🔴 𝐇𝐨𝐰 𝐓𝐨 𝐅𝐨𝐜𝐮𝐬 𝐎𝐧 𝐖𝐡𝐚𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐈𝐧 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠<br>🔗 <a href="https://learnbayesstats.com/episode/124-state-space-models-structural-time-series-jesse-grabowski" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">learnbayesstats.com/episode/12</span><span class="invisible">4-state-space-models-structural-time-series-jesse-grabowski</span></a></p><p>✅ 𝐈𝐧 𝐭𝐡𝐢𝐬 𝐄𝐩𝐢𝐬𝐨𝐝𝐞, 𝐲𝐨𝐮’𝐥𝐥 𝐥𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐭𝐨 𝐚𝐜𝐡𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 with <span class="h-card" translate="no"><a href="https://bayes.club/@pymc" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>pymc</span></a></span> </p><p>Alex Andorra & Jesse Grabowski talk about state space models, simplifying forecasting, applications etc.</p><p><a href="https://mstdn.science/tags/LearningBayesianStatistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LearningBayesianStatistics</span></a> <a href="https://mstdn.science/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> <a href="https://mstdn.science/tags/forecasting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>forecasting</span></a> <a href="https://mstdn.science/tags/timeseries" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>timeseries</span></a></p>
➴➴➴Æ🜔Ɲ.Ƈꭚ⍴𝔥єɼ👩🏻💻<p>I genuinely miss PyMC2. The <a href="https://lgbtqia.space/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> and <a href="https://lgbtqia.space/tags/Arviz" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Arviz</span></a> APIs changes so frequently, that it's impossible to know what the standard approach to anything is.</p><p><a href="https://lgbtqia.space/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://lgbtqia.space/tags/Statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Statistics</span></a> in <a href="https://lgbtqia.space/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> should be easy. </p><p>To be honest, I'd really like a well maintained <a href="https://lgbtqia.space/tags/SkLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SkLearn</span></a> module for it.</p>
Kira Howe (McLean)<p>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.</p><p>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.</p><p><a href="https://indieweb.social/tags/pydata" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pydata</span></a> <a href="https://indieweb.social/tags/pydatalondon" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pydatalondon</span></a> <a href="https://indieweb.social/tags/pymc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pymc</span></a> <a href="https://indieweb.social/tags/bayesianstats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesianstats</span></a></p>
Rami Krispin :unverified:<p>Cohort Revenue & Retention Analysis with Python 🚀</p><p>For those who work with cohort data, I recommend checking Dr.Juan Orduz tutorial for cohort revenue and retention analysis with PyMC 👇🏼</p><p><a href="https://www.pymc-labs.com/blog-posts/cohort-revenue-retention/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">pymc-labs.com/blog-posts/cohor</span><span class="invisible">t-revenue-retention/</span></a></p><p><a href="https://mstdn.social/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://mstdn.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> <a href="https://mstdn.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://mstdn.social/tags/pymc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pymc</span></a></p>
Shih Ching Fu<p><span class="h-card" translate="no"><a href="https://bayes.club/@charleemos" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>charleemos</span></a></span> I have found both the <a href="https://bayes.club/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> tutorials (<a href="https://www.pymc.io/projects/docs/en/latest/guides/Gaussian_Processes.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">pymc.io/projects/docs/en/lates</span><span class="invisible">t/guides/Gaussian_Processes.html</span></a>) and the <a href="https://bayes.club/tags/Stan" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Stan</span></a> User's Guide (<a href="https://mc-stan.org/docs/stan-users-guide/gaussian-processes.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">mc-stan.org/docs/stan-users-gu</span><span class="invisible">ide/gaussian-processes.html</span></a>) on <a href="https://bayes.club/tags/GaussianProcesses" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GaussianProcesses</span></a> good for getting your hands dirty. Seeing GPs in action and fiddling with hyperparameters was helpful for me to understand the mathematical underpinnings.</p>
Jose<p>So testing some intuition on moving a deterministic analysis into a complex <a href="https://fediscience.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> inference problem using <a href="https://fediscience.org/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> has been a really fast process. I'm very impressed with how easy it's been to use it. Well chuffed!!!</p>
Michael Osthege<p>Did you know that <span class="h-card" translate="no"><a href="https://bayes.club/@pymc" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>pymc</span></a></span> has a backend enabling live streaming of MCMCs to a <a href="https://nrw.social/tags/ClickHouse" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ClickHouse</span></a> database?</p><p>Check out <a href="https://github.com/pymc-devs/mcbackend" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">github.com/pymc-devs/mcbackend</span><span class="invisible"></span></a> to learn more about <a href="https://nrw.social/tags/McBackend" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>McBackend</span></a>, and visit <a href="https://github.com/pymc-devs/pymc/wiki/GSoC-2024-projects#Extending-McBackend-related-features" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/pymc-devs/pymc/wiki</span><span class="invisible">/GSoC-2024-projects#Extending-McBackend-related-features</span></a> if you're interested in doing a Google Summer of Code project on this! <a href="https://nrw.social/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> <a href="https://nrw.social/tags/GSoC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GSoC</span></a> <a href="https://nrw.social/tags/GSoC2024" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GSoC2024</span></a></p>
PyMC developers<p>🚀 Introducing "@as_model" in PyMC-Experimental API! </p><p>🔥 Key Features:<br>- Simplifies PyMC modeling<br>- Better code structure</p><p>🔗 Details: GitHub PR #268 <a href="https://github.com/pymc-devs/pymc-experimental/pull/268" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/pymc-devs/pymc-expe</span><span class="invisible">rimental/pull/268</span></a></p><p>🙌 Thanks to Theo Rashid, <span class="h-card" translate="no"><a href="https://bayes.club/@ricardoV94" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>ricardoV94</span></a></span>, Rob Zinkov, <span class="h-card" translate="no"><a href="https://bayes.club/@twiecki" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>twiecki</span></a></span> and Maxim Kochurov !</p><p><a href="https://bayes.club/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> <a href="https://bayes.club/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://bayes.club/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> <a href="https://bayes.club/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://bayes.club/tags/OpenSource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OpenSource</span></a> 🐍📈🎉</p>
Gabriel Weindel<p>Modern <a href="https://fediscience.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> samplers like <a href="https://fediscience.org/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> are too fast... no way to take a break and having the impression to get things done in parallel... barely the time to toot.</p>
Rob Zinkov<p>At the encouragement of <span class="h-card" translate="no"><a href="https://chaos.social/@nkaretnikov" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>nkaretnikov</span></a></span> I made a blogpost on how to use <a href="https://bayes.club/tags/pymc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pymc</span></a> in a more functional style!</p><p><a href="https://www.zinkov.com/posts/2023-alternative-frontends-pymc/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">zinkov.com/posts/2023-alternat</span><span class="invisible">ive-frontends-pymc/</span></a></p>
PyMC developers<p>🚀 Exciting update for <a href="https://bayes.club/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> enthusiasts! Abuzar has pre-recorded a detailed walkthrough on Changepoint Modeling with <a href="https://bayes.club/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a>. 📊</p><p>🔗 Dive in before the live event for a head-start: <a href="https://www.youtube.com/watch?v=iwNju1o5yQo" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">youtube.com/watch?v=iwNju1o5yQ</span><span class="invisible">o</span></a></p><p>📓 Grab the notebook: <a href="https://github.com/abuzarmahmood/pymcon_bayesian_changepoint/blob/72102ad6149b86d586595bf4523f40f66eb20c25/Bayesian_Changepoint_Zoo_neural_data.ipynb" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/abuzarmahmood/pymco</span><span class="invisible">n_bayesian_changepoint/blob/72102ad6149b86d586595bf4523f40f66eb20c25/Bayesian_Changepoint_Zoo_neural_data.ipynb</span></a></p><p>📅 Join us live for deeper insights and a Q&A session! 👉 <a href="https://www.meetup.com/pymc-online-meetup/events/297203071" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">meetup.com/pymc-online-meetup/</span><span class="invisible">events/297203071</span></a></p><p><a href="https://bayes.club/tags/ChangepointModelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ChangepointModelling</span></a> <a href="https://bayes.club/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://bayes.club/tags/StatisticalModelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>StatisticalModelling</span></a></p>
PyMC developers<p>🤔 How does our brain turn flavors into data? Uncover the science with Dr. Abuzar Mahmood at <a href="https://bayes.club/tags/PyMCon" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMCon</span></a>.</p><p>🎥 Watch the interview: <a href="https://www.youtube.com/watch?v=ySF3X45XRyQ" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">youtube.com/watch?v=ySF3X45XRy</span><span class="invisible">Q</span></a><br>🧠 Get into the nitty-gritty of brain signal analysis using <a href="https://bayes.club/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a>.<br>👉 Details & chat: <a href="https://discourse.pymc.io/t/13251" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">discourse.pymc.io/t/13251</span><span class="invisible"></span></a></p><p><a href="https://bayes.club/tags/neuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>neuroscience</span></a> <a href="https://bayes.club/tags/datascience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>datascience</span></a> <a href="https://bayes.club/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://bayes.club/tags/learning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>learning</span></a></p>
PyMC developers<p>📢 Calling all data science enthusiasts and PyMC users!</p><p>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.</p><p>📆 Save the date: Nov 17, 3pm CET / 14 UTC / 6am PT / 9am ET<br>🔗 Sign up here: <a href="https://www.meetup.com/pymc-online-meetup/events/297172683/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">meetup.com/pymc-online-meetup/</span><span class="invisible">events/297172683/</span></a><br>👉 Join the PyMC Discord Server: <a href="https://discord.gg/g9vefGNEMH" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">discord.gg/g9vefGNEMH</span><span class="invisible"></span></a></p><p>🤝 Lets meet, collaborate, and network with fellow Bayesian enthusiasts. <a href="https://bayes.club/tags/pymc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pymc</span></a></p>
Hector Muñoz<p>New post (and blog)! Predicting my dog's weight with Bayesian models.</p><p><a href="https://www.probablycredible.com/blog/bayesian-model-dog-weight/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">probablycredible.com/blog/baye</span><span class="invisible">sian-model-dog-weight/</span></a></p><p><a href="https://bayes.club/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://bayes.club/tags/pymc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pymc</span></a></p>
PyMC developers<p>📢 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</p><p>📅 Date: Wednesday, 1st Nov, 2023 <br>⏰ Time: 19 UTC / 12 pm PT / 3 pm ET <br>📍 Where: Online, on Zoom<br>👉 Register (for Zoom link): <a href="https://www.meetup.com/pymc-online-meetup/events/296914851/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">meetup.com/pymc-online-meetup/</span><span class="invisible">events/296914851/</span></a></p><p>Office hours will last about an hour, so don't worry if you can't make it at exactly this time! see you there</p><p><a href="https://bayes.club/tags/pymc" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pymc</span></a> <a href="https://bayes.club/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a> <a href="https://bayes.club/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a></p>
PyMC developers<p>🚀 PyMC 5.8.0 is here, packed with some fantastic updates and improvements. 🎉</p><p>🆕 New Features:<br>1️⃣ Causal inference: added the do operator for modeling interventions<br>2️⃣ Added ICAR distribution<br>3️⃣ Added JAX implementation for MatrixIsPositiveDefinite Op</p><p>📒 New example NB 👉 Faster Sampling with JAX and Numba, <a href="https://www.pymc.io/projects/examples/en/latest/samplers/fast_sampling_with_jax_and_numba.html" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">pymc.io/projects/examples/en/l</span><span class="invisible">atest/samplers/fast_sampling_with_jax_and_numba.html</span></a><br>... And many more exciting updates, view the summary of changes here 👉 <a href="https://github.com/pymc-devs/pymc/releases/tag/v5.8.0" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/pymc-devs/pymc/rele</span><span class="invisible">ases/tag/v5.8.0</span></a></p><p><a href="https://bayes.club/tags/PyMC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyMC</span></a> <a href="https://bayes.club/tags/datascience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>datascience</span></a> <a href="https://bayes.club/tags/machinelearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>machinelearning</span></a> <a href="https://bayes.club/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://bayes.club/tags/opensource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>opensource</span></a></p>
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