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Contenuto fornito da Alexandre Andorra. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Alexandre Andorra o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
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#118 Exploring the Future of Stan, with Charles Margossian & Brian Ward

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Manage episode 447603563 series 2635823
Contenuto fornito da Alexandre Andorra. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Alexandre Andorra o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!


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:

  • User experience is crucial for the adoption of Stan.
  • Recent innovations include adding tuples to the Stan language, new features and improved error messages.
  • Tuples allow for more efficient data handling in Stan.
  • Beginners often struggle with the compiled nature of Stan.
  • Improving error messages is crucial for user experience.
  • BridgeStan allows for integration with other programming languages and makes it very easy for people to use Stan models.
  • Community engagement is vital for the development of Stan.
  • New samplers are being developed to enhance performance.
  • The future of Stan includes more user-friendly features.

Chapters:

00:00 Introduction to the Live Episode

02:55 Meet the Stan Core Developers

05:47 Brian Ward's Journey into Bayesian Statistics

09:10 Charles Margossian's Contributions to Stan

11:49 Recent Projects and Innovations in Stan

15:07 User-Friendly Features and Enhancements

18:11 Understanding Tuples and Their Importance

21:06 Challenges for Beginners in Stan

24:08 Pedagogical Approaches to Bayesian Statistics

30:54 Optimizing Monte Carlo Estimators

32:24 Reimagining Stan's Structure

34:21 The Promise of Automatic Reparameterization

35:49 Exploring BridgeStan

40:29 The Future of Samplers in Stan

43:45 Evaluating New Algorithms

47:01 Specific Algorithms for Unique Problems

50:00 Understanding Model Performance

54:21 The Impact of Stan on Bayesian Research

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 Caprani, Colin...

  continue reading

182 episodi

Artwork
iconCondividi
 
Manage episode 447603563 series 2635823
Contenuto fornito da Alexandre Andorra. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Alexandre Andorra o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!


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:

  • User experience is crucial for the adoption of Stan.
  • Recent innovations include adding tuples to the Stan language, new features and improved error messages.
  • Tuples allow for more efficient data handling in Stan.
  • Beginners often struggle with the compiled nature of Stan.
  • Improving error messages is crucial for user experience.
  • BridgeStan allows for integration with other programming languages and makes it very easy for people to use Stan models.
  • Community engagement is vital for the development of Stan.
  • New samplers are being developed to enhance performance.
  • The future of Stan includes more user-friendly features.

Chapters:

00:00 Introduction to the Live Episode

02:55 Meet the Stan Core Developers

05:47 Brian Ward's Journey into Bayesian Statistics

09:10 Charles Margossian's Contributions to Stan

11:49 Recent Projects and Innovations in Stan

15:07 User-Friendly Features and Enhancements

18:11 Understanding Tuples and Their Importance

21:06 Challenges for Beginners in Stan

24:08 Pedagogical Approaches to Bayesian Statistics

30:54 Optimizing Monte Carlo Estimators

32:24 Reimagining Stan's Structure

34:21 The Promise of Automatic Reparameterization

35:49 Exploring BridgeStan

40:29 The Future of Samplers in Stan

43:45 Evaluating New Algorithms

47:01 Specific Algorithms for Unique Problems

50:00 Understanding Model Performance

54:21 The Impact of Stan on Bayesian Research

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 Caprani, Colin...

  continue reading

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