talk-data.com

🎙️ Speaker: Allen B. Downey \| ⏰ Time: 16:00 UTC / 9:00 am PT / 12:00 pm ET / 6:00 pm Berlin

If you commute by subway, you might have noticed that you can use the number of waiting passengers to predict the time until the next train. If there are fewer passengers than usual, you just missed a train and might have to wait longer. If there are more than usual, it's been a while since the last train, and you expect one soon. But if there are many more than usual, there might be a disruption of service and a long wait!

In this case study, we'll use PyMC to model this scenario. Starting simple, we'll demonstrate a process for developing and testing models incrementally, present some less commonly used PyMC features, and show how a Bayesian model can replicate Bayesian thinking.

Resources

We will assume that webinar participants are familiar with basic PyMC models and distributions like Normal, Poisson, and Gamma.

If you are not familiar with PyMC, you can start with this chapter from Think Bayes, especially the World Cup Problem: https://allendowney.github.io/ThinkBayes2/chap19.html

Or you can run that chapter on Colab https://colab.research.google.com/github/AllenDowney/ThinkBayes2/blob/master/notebooks/chap19_v3.ipynb

📜 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. Allen B. Downey Allen Downey is a Principal Data Scientist at PyMC Labs, professor emeritus at Olin College and the author of several books -- including Think Python, Think Bayes, and Probably Overthinking It -- and a blog about programming and data science. He received a Ph.D. in computer science from the University of California, Berkeley, and Bachelor's and Masters degrees from MIT.

🔗 Connect with Allen B. Downey: 👉 Linkedin: https://www.linkedin.com/in/allendowney/ 👉 Blog: https://www.allendowney.com/blog/ 👉 X: https://twitter.com/AllenDowney

💼 About the Host:

1. Thomas Wiecki (Founder of 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: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://www.pymc-labs.com/ https://twiecki.io/

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

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 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/

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start. Use your programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems