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    #64 Modeling the Climate & Gravity Waves, with Laura Mansfield

    #64 Modeling the Climate & Gravity Waves, with Laura Mansfield

    FromLearning Bayesian Statistics

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    #64 Modeling the Climate & Gravity Waves, with Laura Mansfield

    FromLearning Bayesian Statistics

    ratings:
    Length:
    67 minutes
    Released:
    Jul 20, 2022
    Format:
    Podcast episode

    Description

    Proudly sponsored by https://www.pymc-labs.io/ (PyMC Labs), the Bayesian Consultancy. https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf (Book a call), or get in touch!
    I’m sure you’ve already heard of gravitational waves, because my listeners are the coolest and smartest ever ;) But did you know about gravity waves? That’s right, waves in the sky due to gravity — sounds awesome, right?
    Well, I’m pretty sure that Laura Mansfield will confirm your prior. Currently a postdoc at Stanford University, Laura studies — guess what? — gravity waves and how they are represented in climate models. In particular, she uses Bayesian methods to estimate the uncertainty on the gravity wave components of the models.
    Holding a PhD from the University of Reading in the UK, her background is in atmospheric physics, but she’s interested in climate change and environmental issues.
    So seat back, chill out, and enjoy this physics-packed, aerial episode!
    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, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha and Scott Anthony Robson.
    Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
    Links from the show:
    Laura on Twitter: https://twitter.com/lau_mansfield (https://twitter.com/lau_mansfield)
    Laura’s webpage: https://profiles.stanford.edu/laura-mansfield (https://profiles.stanford.edu/laura-mansfield)
    Julia package for Gaussian Processes: https://github.com/STOR-i/GaussianProcesses.jl (https://github.com/STOR-i/GaussianProcesses.jl) 
    Julia implementation of the scikit-learn API: https://github.com/cstjean/ScikitLearn.jl (https://github.com/cstjean/ScikitLearn.jl)
    Derivative-free Bayesian optimization techniques based on Ensemble Kalman Filters: https://github.com/CliMA/EnsembleKalmanProcesses.jl (https://github.com/CliMA/EnsembleKalmanProcesses.jl)



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

    Podcorn - https://podcorn.com/privacy
    Released:
    Jul 20, 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