Fisher, Bayes, and predictive Bayesian inference [seminar]

An interesting Foundations of Probability seminar at Rutgers University this Monday, at 4:30ET, 8:30GMT, by Sandy Zabell (the password is Angelina’s birthdate): R. A. Fisher is usually perceived to have been a staunch critic of the Bayesian approach to statistics, yet his last book (Statistical Methods and Scientific Inference, 1956) is much closer in spirit […]

we have never been unable to develop a reliable predictive model

An alarming entry in The Guardian about the huge proportion of councils in the UK using machine-learning software to allocate benefits, detect child abuse or claim fraud. And relying blindly on the outcome of such software, despite their well-documented lack of reliability, uncertainty assessments, and warnings. Blindly in the sense that the impact of their […]

projection predictive input variable selection

Juho Piironen and Aki Vehtari just arXived a paper on variable selection that relates to two projection papers we wrote in the 1990’s with Costas Goutis (who died near Seattle in a diving accident on July 1996) and Jérôme Dupuis… Except that they move to the functional space of Gaussian processes. The covariance function in […]

a unified treatment of predictive model comparison

“Applying various approximation strategies to the relative predictive performance derived from predictive distributions in frequentist and Bayesian inference yields many of the model comparison techniques ubiquitous in practice, from predictive log loss cross validation to the Bayesian evidence and Bayesian information criteria.” Michael Betancourt (Warwick) just arXived a paper formalising predictive model comparison in an […]

comparison of Bayesian predictive methods for model selection

“Dupuis and Robert (2003) proposed choosing the simplest model with enough explanatory power, for example 90%, but did not discuss the effect of this threshold for the predictive performance of the selected models. We note that, in general, the relative explanatory power is an unreliable indicator of the predictive performance of the submodel,” Juho Piironen […]