🎙️ Speaker: Will Dean\, Thomas Wiecki \| ⏰ Time: 16:00 UTC / 9am PT / 12pm ET / 6pm Berlin

Discuss using a classical Natural Language Processing technique for modeling weekly calendar data through a shift in vocabulary (pun intended). By using Latent Dirichlet Allocation to model discretized calendar events, the model’s probabilistic origin and Bayesian connection can be leveraged for various applications and insights.

For a sneak peek, check out the "latent-calendar" on PyPI or see an example here: https://wd60622.github.io/latent-calendar/examples/datasets/bikes-in-chicago/

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Will Dean Senior Data Scientist at FREENOW Will Dean is a Statistician and Data Scientist with experience in geospatial and user analytics. He is passionate about Bayesian methods and using data visualization to tell a story. He is interested in software design and how it can make data problems easier and more enjoyable to solve.

🔗 Connect with Will Dean: 👉 LinkedIn: https://www.linkedin.com/in/williambdean/ 👉 GitHub: https://github.com/wd60622

1. Dr. Thomas Wiecki (PyMC Labs)

Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 Website: https://www.pymc-labs.com/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🔗 Connecting with PyMC Open Source: 💬 Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc 💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany 🐥 Twitter: https://twitter.com/pymc_devs 📺 YouTube: https://www.youtube.com/c/PyMCDevelopers 🎉 Meetup: https://www.meetup.com/pymc-online-meetup/