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Michael D. Lee’s Followers (1)
Michael D. Lee
* Note: these are all the books on Goodreads for this author. To add more, click here.
“Instead of “integrating over the posterior,” orthodox methods often use the “plug-in principle.” In this case, the plug-in principle suggests that we predict solely based on , the maximum likelihood estimate. Why is this generally a bad idea? Can you think of a specific situation in which this may not be so much of a problem?”
― Bayesian Cognitive Modeling: A Practical Course
― Bayesian Cognitive Modeling: A Practical Course
“Chapter 6: Latent-mixture models • Ortega, A., Wagenmakers, E.-J., Lee, M. D., Markowitsch, H. J., & Piefke, M. (2012). A Bayesian latent group analysis for detecting poor effort in the assessment of malingering. Archives of Clinical Neuropsychology, 27, 453–465.”
― Bayesian Cognitive Modeling: A Practical Course
― Bayesian Cognitive Modeling: A Practical Course
“On his blog, prominent Bayesian Andrew Gelman wrote (March 18, 2010): “Some probabilities are more objective than others. The probability that the die sitting in front of me now will come up ‘6’ if I roll it …that’s about . But not exactly, because it’s not a perfectly symmetric die. The probability that I’ll be stopped by exactly three traffic lights on the way to school tomorrow morning: that’s well, I don’t know exactly, but it is what it is.” Was de Finetti wrong, and is there only one clearly defined probability of Andrew Gelman encountering three traffic lights on the way to school tomorrow morning?”
― Bayesian Cognitive Modeling: A Practical Course
― Bayesian Cognitive Modeling: A Practical Course
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