Thomas Wiecki's guest appearances

Thomas Wiecki

Learning Bayesian Statistics

David Spiegelhalter

Andrew Gelman

Michael Lee

Baba Brinkman

Michael Kennedy

Allen Downey

Sean Taylor

Michael Faber

Matthew Kay

Michael Betancourt

Eric Ma

Karin Knudson

Colin Carroll

Osvaldo Martin

Chris Fonnesbeck

OwlTail

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:Thomas’ series on Hierarchical Regression: https://twiecki.io/blog/2013/08/12/bayesian-glms-1/Non-centered Parametrization with PyMC3: https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/Using Bayesian Decision Making: https://twiecki.io/blog/2019/01/14/supply_chain/PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/Symbolic PyMC: https://pymc-devs.github.io/symbolic-pymc/PyData Deep Dive Podcast: https://pydata-podcast.comThomas on Twitter: https://twitter.com/twiecki?lang=enThomas on Patreon: https://www.patreon.com/twieckiThomas on GitHub: https://github.com/twieckiAlex’s Hierarchical Model of Elections in Paris: https://mybinder.org/v2/gh/AlexAndorra/pollsposition_models/master?urlpath=%2Fvoila%2Frender%2Fdistrict-level%2Fmunic_model_analysis.ipynbThis podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

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 <a href="https://bababrinkman.com/" rel="noopener noreferrer nofollow" target="_blank">https://bababrinkman.com/</a> !Links from the show:<ul><li>Thomas’ series on Hierarchical Regression: <a href="https://twiecki.io/blog/2013/08/12/bayesian-glms-1/" rel="noopener noreferrer nofollow" target="_blank">https://twiecki.io/blog/2013/08/12/bayesian-glms-1/</a></li><li>Non-centered Parametrization with PyMC3: <a href="https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/" rel="noopener noreferrer nofollow" target="_blank">https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/</a></li><li>Using Bayesian Decision Making: <a href="https://twiecki.io/blog/2019/01/14/supply_chain/" rel="noopener noreferrer nofollow" target="_blank">https://twiecki.io/blog/2019/01/14/supply_chain/</a></li><li>PyMC3 - Probabilistic Programming in Python: <a href="https://docs.pymc.io/" rel="noopener noreferrer nofollow" target="_blank">https://docs.pymc.io/</a></li><li>Symbolic PyMC: <a href="https://pymc-devs.github.io/symbolic-pymc/" rel="noopener noreferrer nofollow" target="_blank">https://pymc-devs.github.io/symbolic-pymc/</a></li><li>PyData Deep Dive Podcast: <a href="https://pydata-podcast.com" rel="noopener noreferrer nofollow" target="_blank">https://pydata-podcast.com</a></li><li>Thomas on Twitter: <a href="https://twitter.com/twiecki?lang=en" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/twiecki?lang=en</a></li><li>Thomas on Patreon: <a href="https://www.patreon.com/twiecki" rel="noopener noreferrer nofollow" target="_blank">https://www.patreon.com/twiecki</a></li><li>Thomas on GitHub: <a href="https://github.com/twiecki" rel="noopener noreferrer nofollow" target="_blank">https://github.com/twiecki</a></li><li>Alex’s Hierarchical Model of Elections in Paris: <a href="https://mybinder.org/v2/gh/AlexAndorra/pollsposition_models/master?urlpath=%2Fvoila%2Frender%2Fdistrict-level%2Fmunic_model_analysis.ipynb" rel="noopener noreferrer nofollow" target="_blank">https://mybinder.org/v2/gh/AlexAndorra/pollsposition_models/master?urlpath=%2Fvoila%2Frender%2Fdistrict-level%2Fmunic_model_analysis.ipynb</a></li></ul>This podcast uses the following third-party services for analysis: Podcorn - <a href="https://podcorn.com/privacy" rel="nofollow" target="_blank">https://podcorn.com/privacy</a>

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