• #123 BART & The Future of Bayesian Tools, with Osvaldo Martin

  • 2025/01/10
  • 再生時間: 1 時間 32 分
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#123 BART & The Future of Bayesian Tools, with Osvaldo Martin

  • サマリー

  • 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

    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Takeaways:

    • BART models are non-parametric Bayesian models that approximate functions by summing trees.
    • BART is recommended for quick modeling without extensive domain knowledge.
    • PyMC-BART allows mixing BART models with various likelihoods and other models.
    • Variable importance can be easily interpreted using BART models.
    • PreliZ aims to provide better tools for prior elicitation in Bayesian statistics.
    • The integration of BART with Bambi could enhance exploratory modeling.
    • Teaching Bayesian statistics involves practical problem-solving approaches.
    • Future developments in PyMC-BART include significant speed improvements.
    • Prior predictive distributions can aid in understanding model behavior.
    • Interactive learning tools can enhance understanding of statistical concepts.
    • Integrating PreliZ with PyMC improves workflow transparency.
    • Arviz 1.0 is being completely rewritten for better usability.
    • Prior elicitation is crucial in Bayesian modeling.
    • Point intervals and forest plots are effective for visualizing complex data.

    Chapters:

    00:00 Introduction to Osvaldo Martin and Bayesian Statistics

    08:12 Exploring Bayesian Additive Regression Trees (BART)

    18:45 Prior Elicitation and the PreliZ Package

    29:56 Teaching Bayesian Statistics and Future Directions

    45:59 Exploring Prior Predictive Distributions

    52:08 Interactive Modeling with PreliZ

    54:06 The Evolution of ArviZ

    01:01:23 Advancements in ArviZ 1.0

    01:06:20 Educational Initiatives in Bayesian Statistics

    01:12:33 The Future of Bayesian Methods

    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...

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あらすじ・解説

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

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

  • BART models are non-parametric Bayesian models that approximate functions by summing trees.
  • BART is recommended for quick modeling without extensive domain knowledge.
  • PyMC-BART allows mixing BART models with various likelihoods and other models.
  • Variable importance can be easily interpreted using BART models.
  • PreliZ aims to provide better tools for prior elicitation in Bayesian statistics.
  • The integration of BART with Bambi could enhance exploratory modeling.
  • Teaching Bayesian statistics involves practical problem-solving approaches.
  • Future developments in PyMC-BART include significant speed improvements.
  • Prior predictive distributions can aid in understanding model behavior.
  • Interactive learning tools can enhance understanding of statistical concepts.
  • Integrating PreliZ with PyMC improves workflow transparency.
  • Arviz 1.0 is being completely rewritten for better usability.
  • Prior elicitation is crucial in Bayesian modeling.
  • Point intervals and forest plots are effective for visualizing complex data.

Chapters:

00:00 Introduction to Osvaldo Martin and Bayesian Statistics

08:12 Exploring Bayesian Additive Regression Trees (BART)

18:45 Prior Elicitation and the PreliZ Package

29:56 Teaching Bayesian Statistics and Future Directions

45:59 Exploring Prior Predictive Distributions

52:08 Interactive Modeling with PreliZ

54:06 The Evolution of ArviZ

01:01:23 Advancements in ArviZ 1.0

01:06:20 Educational Initiatives in Bayesian Statistics

01:12:33 The Future of Bayesian Methods

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...

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