Tag Archives: 0.05

Michael Lee

The Bayes Factor
The Bayes Factor
Michael Lee
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In this episode JP and Alex interview Michael Lee. They discuss model complexity and generative models, the differences between cognitive models and machine learning, whether and when preregistration of models is useful, and Michael’s undying love of cricket.

  • Follow us on Twitter: https://twitter.com/TheBayesFactor
  • Follow us on Facebook: https://www.facebook.com/TheBayesFactor/
  • Subscribe & leave us a review on iTunes: https://itunes.apple.com/us/podcast/the-bayes-factor/id1308207723

Notes and links:

  • Follow Michael on twitter: https://twitter.com/mdlBayes
  • The workshop we attended resulted in this special issue on robust modeling in cognitive science: https://link.springer.com/journal/42113/volumes-and-issues/2-3
  • Lee and Vanpaemel on informative priors: https://link.springer.com/article/10.3758/s13423-017-1238-3

Zoltan Dienes

The Bayes Factor
The Bayes Factor
Zoltan Dienes
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In this episode JP and Alex interview Zoltan Dienes. They discuss Zoltan's passion for the martial arts, 
why Bayesian inference could be more Popperian than you might think, 
and the easiest way to start using Bayesian statistics in practice.

- Follow us on Twitter: https://twitter.com/TheBayesFactor
- Follow us on Facebook: https://www.facebook.com/TheBayesFactor/
- Subscribe & leave us a review on iTunes: https://itunes.apple.com/us/podcast/the-bayes-factor/id1308207723

Notes and links:

- Zoltan’s book: https://www.amazon.com/Understanding-Psychology-Science-Introduction-Statistical/dp/023054231X
- Zoltan’s Bayes references and Bayes factor calculator: http://www.lifesci.sussex.ac.uk/home/Zoltan_Dienes/inference/Bayes.htm
- Four reasons to prefer Bayesian over significance testing: https://link.springer.com/article/10.3758/s13423-017-1266-z