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#59 Bayesian Modeling in Civil Engineering, with Michael Faber

#59 Bayesian Modeling in Civil Engineering, with Michael Faber

FromLearning Bayesian Statistics


#59 Bayesian Modeling in Civil Engineering, with Michael Faber

FromLearning Bayesian Statistics

ratings:
Length:
59 minutes
Released:
Apr 14, 2022
Format:
Podcast episode

Description

In large-scale one-off civil infrastructure, decision-making under uncertainty is part of the job, that’s just how it is. But, civil engineers don't get the luxury of building 10^6 versions of the bridge, offshore wind turbine or aeronautical structure to consider a relative frequency interpretation!
And as you’ll hear, challenges don’t stop there: you also have to consider natural hazards such as earthquakes, rockfall and typhoons — in case you were wondering, civil engineering is not among the boring jobs!
To talk about these original topics, I had the pleasure to host Michael Faber. Michael is a Professor at the Department of Built Environment at Aalborg University, Denmark, the President of the Joint Committee on Structural Safety and is a tremendously deep thinker on the Bayesian interpretation of probability as it pertains to the risk-informed management of big infrastructure.
His research interests are directed on governance and management of risks, resilience and sustainability in the built environment — doing all that with Bayesian probabilistic modeling and applied Bayesian decision analysis, as you’ll hear.
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/) !
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 and Aubrey Clayton.
Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
Links from the show:
Michael's profile on Aalborg University: https://vbn.aau.dk/en/persons/100493 (https://vbn.aau.dk/en/persons/100493)
Michael's LinkedIn profile: https://www.linkedin.com/in/michael-havbro-faber-22898414/ (https://www.linkedin.com/in/michael-havbro-faber-22898414/)
Statistics and Probability Theory - In Pursuit of Engineering Decision Support: https://link.springer.com/book/10.1007/978-94-007-4056-3 (https://link.springer.com/book/10.1007/978-94-007-4056-3)
Bayes in Civil Engineering - an abridged personal account of research and applications: https://www.linkedin.com/pulse/bayes-civil-engineering-michael-havbro-faber (https://www.linkedin.com/pulse/bayes-civil-engineering-michael-havbro-faber)
Website of the Joint Committee on Structural Safety (JCSS): https://www.jcss-lc.org/ (https://www.jcss-lc.org/)



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Released:
Apr 14, 2022
Format:
Podcast episode

Titles in the series (100)

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Paris. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy