#11 Taking care of your Hierarchical Models, with Thomas Wiecki

I bet you already heard about hierarchical models, or multilevel models, or varying-effects models — yeah this type of models has a lot of names! Many people even turn to Bayesian tools to build _exactly_ these models. But what are they? How do you build and use a hierarchical model? What are the tricks and classical traps? And even more important: how do you _interpret_ a hierarchical model?

In this episode, Thomas Wiecki will come to the rescue and explain what multilevel models are, how to build them, what their powers are… but also why you should be very careful when building them…

Does the name Thomas Wiecki ring a bell? Probably because he’s the host and creator of the PyData Deep Dive Podcast, where he interviews open-source contributors from the Python and Data Science worlds! Thomas is also the VP of Data Science at Quantopian, a crowd-sourced quantitative investment firm that encourages people everywhere to write investment algorithms.

Finally, Thomas is a longtime Bayesian and core-developer of PyMC3, a fantastic python package to do probabilistic programming in Python. On his blog, he publishes tutorial articles and explores new ideas such as Bayesian Deep Learning. Caring a lot about open-source software sustainability, he puts all he’s up to on his Patreon page, that you’ll find in the show notes.

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:

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Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Estonia. By day, I'm a data scientist and modeler at the PyMC Labs consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love election forecasting and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and unlock exclusive Bayesian swag on Patreon!