Demetri Pananos's guest appearances

Demetri Pananos

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

Do you know Google Summer of Code? It’s a time of year when students can contribute to open-source software by developing and adding much needed functionalities to the open-source package of their choice. And Demetri Pananos did just that.He did it in 2019 with PyMC3, for which he developed the API for ordinary differential equations. In this episode, he’ll tell us why and how he did that, what he learned from the experience, and what the strengths and weaknesses of the API are in his opinion.Demetri is a Ph.D candidate in Biostatistics at Western University, in Ontario, Canada. His research interests surround machine learning and Bayesian statistics for personalized medicine. He earned his Master’s in Applied Mathematics from The University of Waterloo and is a firm believer in open science, interdisciplinary collaboration, and reproducible research. Other than that, he loves plotting data and drinking IPA beer – well, who doesn’t?”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:Demetri on Twitter: https://twitter.com/PhDemetriDemetri on GitHub: https://github.com/DpananosDemetri's website: https://dpananos.github.io/PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/Chris Bishop, Pattern Recognition and Machine Learning: https://www.amazon.fr/Pattern-Recognition-Machine-Learning-Christopher/dp/0387310738Bayesian Data Analysis (Gelman, Carlin, Stern, Dunson, Vehtari, Rubin): http://www.stat.columbia.edu/~gelman/book/Parallel Plots: https://arviz-devs.github.io/arviz/generated/arviz.plot_parallel.htmlThis podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

Do you know Google Summer of Code? It’s a time of year when students can contribute to open-source software by developing and adding much needed functionalities to the open-source package of their choice. And Demetri Pananos did just that.He did it in 2019 with PyMC3, for which he developed the API for ordinary differential equations. In this episode, he’ll tell us why and how he did that, what he learned from the experience, and what the strengths and weaknesses of the API are in his opinion.Demetri is a Ph.D candidate in Biostatistics at Western University, in Ontario, Canada. His research interests surround machine learning and Bayesian statistics for personalized medicine. He earned his Master’s in Applied Mathematics from The University of Waterloo and is a firm believer in open science, interdisciplinary collaboration, and reproducible research. Other than that, he loves plotting data and drinking IPA beer – well, who doesn’t?”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>Demetri on Twitter: <a href="https://twitter.com/PhDemetri" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/PhDemetri</a></li><li>Demetri on GitHub: <a href="https://github.com/Dpananos" rel="noopener noreferrer nofollow" target="_blank">https://github.com/Dpananos</a></li><li>Demetri's website: <a href="https://dpananos.github.io/" rel="noopener noreferrer nofollow" target="_blank">https://dpananos.github.io/</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>Chris Bishop, Pattern Recognition and Machine Learning: <a href="https://www.amazon.fr/Pattern-Recognition-Machine-Learning-Christopher/dp/0387310738" rel="noopener noreferrer nofollow" target="_blank">https://www.amazon.fr/Pattern-Recognition-Machine-Learning-Christopher/dp/0387310738</a></li><li>Bayesian Data Analysis (Gelman, Carlin, Stern, Dunson, Vehtari, Rubin): <a href="http://www.stat.columbia.edu/~gelman/book/" rel="noopener noreferrer nofollow" target="_blank">http://www.stat.columbia.edu/~gelman/book/</a></li><li>Parallel Plots: <a href="https://arviz-devs.github.io/arviz/generated/arviz.plot_parallel.html" rel="noopener noreferrer nofollow" target="_blank">https://arviz-devs.github.io/arviz/generated/arviz.plot_parallel.html</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>

#12 Biostatistics and Differential Equations, with Demetri Pananos