Episode
#104 Automated Gaussian Processes & Sequential Monte Carlo, with Feras Saad
- Podcast
- Learning Bayesian Statistics
- Published
- Apr 16, 2024
- Duration seconds
- 5448
- Processing state
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Summary
Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! My Intuitive Bayes Online Courses 1:1 Mentorship with me GPs are extremely powerful…. but hard to handle. One of the bottlenecks is learning the appropriate kernel. What if you could learn the structure of GP kernels automatically? Sounds really cool, but also a bit futuristic, doesn’t it? Well, think again, because in this episode, Feras Saad will teach us how to do just that! Feras is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. He received his PhD in Computer Science from MIT, and, most importantly for our conversation, he’s the creator of AutoGP.jl, a Julia package for automatic Gaussian process modeling. Feras discusses the implementation of AutoGP, how it scales, what you can do with it, and how you can integrate its outputs in your models. Finally, Feras provides an overview of Sequential Monte Carlo and its usefulness in AutoGP, highlighting the ability of SMC to incorporate new data in a streaming fashion and explore multiple modes efficiently. Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ ! Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith,…