#7 Designing a Probabilistic Programming Language & Debugging a Model, with Junpeng Lao

Learning Bayesian Statistics

You can’t study psychology up until your PhD and end-up doing very mathematical and computational data science at Google right? It’s too hard of a U-turn — some would even say it’s NUTS, just because they like bad puns… Well think again, because Junpeng Lao did just that!

Before doing data science at Google, Junpeng was a cognitive psychology researcher at the University of Fribourg, Switzerland. Working in Python, Matlab and occasionally in R, Junpeng is a prolific open-source contributor, particularly to the popular TensorFlow and PyMC3 libraries. He also maintains the PyMC Discourse on his free time, where he amazingly answers all kinds of various and very specific questions!

In this episode, he’ll tell you what the core characteristics of TensorFlow Probability are, and when you would use TFP instead of another probabilistic programming framework, like Stan or PyMC3. He’ll also explain why PyMC4 will be based on TensorFlow Probability itself, and what future contributions he has in mind for these two amazing libraries. Finally, Junpeng will share with you his workflow for debugging a model, or just for better understanding your models.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Links from the show:

• Junpeng's blog: https://junpenglao.xyz/
• Junpeng on Twitter: https://twitter.com/junpenglao
• Junpeng on GitHub: https://github.com/junpenglao
• Advanced Bayesian Modeling Tutorial: https://discourse.pymc.io/t/advance-bayesian-modelling-with-pymc3/1439
• Stan Devs' Prior Choice Recommendations: https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations
• PyMC Discourse: https://discourse.pymc.io/
• PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/
• Tensor Flow Probability: https://www.tensorflow.org/probability/