#47 Bayes in Physics & Astrophysics, with JJ Ruby
Learning Bayesian Statistics
Is There an SMC Sampler?
Piro's research is focused on introducing patient sthats into experimental analysis. He uses piemc three s ininety nine per cent of the time, it just works. Piro says he prefers to reduce his data as little as possible and then marry that with a statistical model. "I think jack's a slash om nom piro"
The field of physics has brought tremendous advances to modern Bayesian statistics, especially inspiring the current algorithms enabling all of us to enjoy the Bayesian power on our own laptops.
I did receive some physicians already on the show, like Michael Betancourt in episode 6, but in my legendary ungratefulness I hadn’t dedicated a whole episode to talk about physics yet.
Well that’s now taken care of, thanks to JJ Ruby. Apart from having really good tastes (he’s indeed a fan of this very podcast), JJ is currently a postdoctoral fellow for the Center for Matter at Atomic Pressures at the University of Rochester, and will soon be starting as a Postdoctoral Scholar at Lawrence Livermore National Laboratory, a U.S. Department of Energy National Laboratory.
JJ did his undergraduate work in Astrophysics and Planetary Science at Villanova University, outside of Philadelphia, and completed his master’s degree and PhD in Physics at the University of Rochester, in New York.
JJ studies high energy density physics and focuses on using Bayesian techniques to extract information from large scale physics experiments with highly integrated measurements.
In his freetime, he enjoys playing sports including baseball, basketball, and golf.
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, 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, 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, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin and Cameron Smith.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- Center for Matter at Atomic Pressures: https://www.rochester.edu/cmap/
- Laboratory for Laser Energetics: https://www.lle.rochester.edu/index.php/about-the-laboratory-for-laser-energetics/
- Lawrence Livermore National Laboratory: https://www.llnl.gov/
- JJ's thesis -- Bayesian Inference of Fundamental Physics at Extreme Conditions: https://www.lle.rochester.edu/media/publications/documents/theses/Ruby.pdf
- Recent Fusion Breakthrough: https://www.llnl.gov/news/national-ignition-facility-experiment-puts-researchers-threshold-fusion-ignition
- LBS #6, A principled Bayesian workflow, with Michael Betancourt: https://www.learnbayesstats.com/episode/6-a-principled-bayesian-workflow-with-michael-betancourt
- 20 Best Statistics Podcasts of 2021: https://welpmagazine.com/20-best-statistics-podcasts-of-2021/
- E.T. Jaynes, Probability Theory -- The Logic of Science: https://www.goodreads.com/book/show/151848.Probability_Theory
- D.S. Sivia, Data Analysis -- A Bayesian Tutorial: http://aprsa.villanova.edu/files/sivia.pdf
- S. Chandrasekhar -- An Introduction to the Study of Stellar Structure: https://www.amazon.com/Introduction-Study-Stellar-Structure-Astronomy/dp/0486604136
- Fun quote (from preface in Jaynes' book):
Therefore we think that in the future, workers in all the quantitative sciences will be obliged, as a matter of practical necessity, to use probability theory in the manner expounded here. This trend is already well under way in several fields, ranging from econometrics to astronomy to magnetic resonance spectroscopy; but to make progress in a new area it is necessary to develop a healthy disrespect for tradition and authority, which have retarded progress throughout the 20th century.
Remember Everything You Learn from Podcasts
