On 28th November OxTalks will move to the new Halo platform and will become 'Oxford Events' (full details are available on the Staff Gateway).
There will be an OxTalks freeze beginning on Friday 14th November. This means you will need to publish any of your known events to OxTalks by then as there will be no facility to publish or edit events in that fortnight. During the freeze, all events will be migrated to the new Oxford Events site. It will still be possible to view events on OxTalks during this time.
If you have any questions, please contact halo@digital.ox.ac.uk
To Bayes or not to Bayes?
In many disciplines (e.g., epidemiology, genetics, medicine, many branches of social science) Bayesian statistics are used instead of Maximum Likelihood (ML, by Ronald Aylmer Fisher). Bayesian statistics gives the inferential probability of the parameter estimates from the posterior distribution, given the data. ML gives the sampling probability of the data given the model, also termed “Null Hypothesis Statistical Testing” (NHST). History shows how sampling probability and inferential probability have been used interchangeably, sometimes with serious consequences. Bayesian statistics has many advantages over NHST e.g., (1) it is logical in its philosophy, and (2) it does not rely on large sample theory. Modern software, both free (e.g., R brms) and commercial ones (e.g., SPSS, Mplus), now include Bayesian algorithms. Examples of logical reasoning, advantages of using the Bayesian estimator, and estimation of posterior probabilities using simulated and real-world data are presented in the talk.
