#62 Bayesian Generative Modeling for Healthcare, with Maria Skoularidou
Learning Bayesian Statistics
Introduction
Maria schularadu is a final year pag student at the university of cambridge. Her thesis is focused on probabilistic machine learning, and towards using ternative modelling in healthgear. When she is not working on all this, you will find her playing the neg tricking or rowing. This is learning basion statistics. Episode 62, recorded may fourth 20 22.
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We talk a lot about generative modeling on this podcast — at least since episode 6, with Michael Betancourt! And an area where this way of modeling is particularly useful is healthcare, as Maria Skoularidou will tell us in this episode.
Maria is a final year PhD student at the University of Cambridge. Her thesis is focused on probabilistic machine learning and, more precisely, towards using generative modeling in… you guessed it: healthcare!
But her fields of interest are diverse: from theory and methodology of machine intelligence to Bayesian inference; from theoretical computer science to information theory — Maria is knowledgeable in a lot of topics! That’s why I also had to ask her about mixture models, a category of models that she uses frequently.
Prior to her PhD, Maria studied Computer Science and Statistical Science at Athens University of Economics and Business. She’s also invested in several efforts to bring more diversity and accessibility in the data science world.
When she’s not working on all this, you’ll find her playing the ney, trekking or rawing.
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, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, 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, 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, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton and Jeannine Sue.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- Maria on Twitter: https://twitter.com/skoularidou
- Maria on LinkedIn: https://www.linkedin.com/in/maria-skoularidou-1289b62a/
- Maria’s webpage:
- https://www.mrc-bsu.cam.ac.uk/people/in-alphabetical-order/n-to-s/maria-skoularidou/
- Mixture models in PyMC: https://www.pymc.io/projects/examples/en/latest/gallery.html#mixture-models
- LBS #4 Dirichlet Processes and Neurodegenerative Diseases, with Karin Knudson: https://learnbayesstats.com/episode/4-dirichlet-processes-and-neurodegenerative-diseases-with-karin-knudson/
- Bayesian mixtures with an unknown number of components: https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00095
- Markov Chain sampling methods for Dirichlet Processes: https://www.tandfonline.com/doi/abs/10.1080/10618600.2000.10474879
- Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models: https://academic.oup.com/biomet/article-abstract/95/1/169/219181
- Sampling Dirichlet mixture models with slices: https://www.tandfonline.com/doi/abs/10.1080/03610910601096262
- Label switching problem:
- https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00265
- Mixture Models With a Prior on the Number of Components: https://www.tandfonline.com/doi/abs/10.1080/01621459.2016.1255636
- Approximate Bayesian inference for Gaussian models (R-INLA): https://www.r-inla.org
- Intuitive Bayes Introductory Course: https://www.intuitivebayes.com/
- PyMC Labs corporate workshops: https://www.pymc-labs.io/workshops
- LBS #44 Building Bayesian Models at scale, with Rémi Louf: https://www.learnbayesstats.com/episode/44-bayesian-models-at-scale-remi-louf
- Blackjax – Sampling library designed for ease of use, speed and modularity: https://blackjax-devs.github.io/blackjax/
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