tag:blogger.com,1999:blog-3240271627873788873.post5828179780968913828..comments2024-03-26T06:46:11.752-04:00Comments on Doing Bayesian Data Analysis: A case in which metric data are better analyzed by an ordinal modelJohn K. Kruschkehttp://www.blogger.com/profile/17323153789716653784noreply@blogger.comBlogger1125tag:blogger.com,1999:blog-3240271627873788873.post-42204044992855321832015-09-17T15:11:50.800-04:002015-09-17T15:11:50.800-04:00What about a case where I might have some metric d...What about a case where I might have some metric data, atomic weights in particular, that I predict have a particular value? I've been re-reading some of Scientific Reasoning: The Bayesian Approach by Colin Howson and Peter Urbach, and they relate a story where an atomic weight was predicted to be one thing by a theory, and data showed otherwise, so the way the authors look at it is looking at the posterior of the auxiliary hypothesis and of the general theory, and they show that the posterior for the general theory is less harmed by the data than the auxiliary hypothesis. I want to do a more refined analysis using probability distributions.<br /><br />Let's say here that the predicted variable is exactly 36, with a prior probability of 60% in the auxiliary hypothesis and prior probability of 90% in the theory. The data show that it is a bit less than that (the book gives 35.83, but I've created a data set using a gaussian random number generator to have more data to play with). Using the generalized linear model seems inappropriate to me, but in the DBDA2E book, the GLM is the only area where a single metric predicted variable from a single metric predictor is discussed. To what section of the book would you refer me to do what I want with this toy data?The Professorhttps://www.blogger.com/profile/08642312691051116133noreply@blogger.com