Do you know Turing? Of course you do! With Soss and Gen, it’s one of the blockbusters to do probabilistic programming in Julia. And in this episode Cameron Pfiffer will tell us all about it — how it came to life, how it fits into the probabilistic programming landscape, and what its main strengths and weaknesses are.
Cameron did some Rust, some Python, but he especially loves coding in Julia. That’s also why he’s one of the core-developers of Turing.jl. He’s also a PhD student in finance at the University of Oregon and did his master’s in finance at the University of Reading. His interests are pretty broad, from cryptocurrencies, algorithmic and high-frequency trading, to AI in financial markets and anomaly detection – in a nutshell he’s a fan of topics where technology is involved.
As he’s the first economist to come to the show, I also asked him how Bayesian the field of economics is, why he thinks economics is quite unique among the social sciences, and how economists think about causality — I later learned that this topic is pretty controversial!
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) !
Links from the show:
Bayesian Econometrics on Cameron's Blog: http://cameron.pfiffer.org/2020/03/24/bayesian-econometrics/ (http://cameron.pfiffer.org/2020/03/24/bayesian-econometrics/)
Cameron on Twitter: https://twitter.com/cameron_pfiffer (https://twitter.com/cameron_pfiffer)
Cameron on GitHub: https://github.com/cpfiffer (https://github.com/cpfiffer)
Turing.jl -- Bayesian inference in Julia: https://turing.ml/dev/ (https://turing.ml/dev/)
Gen.jl -- Programmable inference embedded in Julia: https://www.gen.dev/ (https://www.gen.dev/)
Soss.jl -- Probabilistic programming via source rewriting: https://github.com/cscherrer/Soss.jl (https://github.com/cscherrer/Soss.jl)
The Julia Language -- A fresh approach to technical computing: https://julialang.org/ (https://julialang.org/)
What is Probabilistic Programming -- Cornell University: http://adriansampson.net/doc/ppl.html (http://adriansampson.net/doc/ppl.html)
Mostly Harmless Econometrics Book: http://www.mostlyharmlesseconometrics.com/ (http://www.mostlyharmlesseconometrics.com/)

Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.


This podcast uses the following third-party services for analysis:

Podcorn - https://podcorn.com/privacy