a case for Bayesian deep learnin

Andrew Wilson wrote a piece about Bayesian deep learning last winter. Which I just read. It starts with the (posterior) predictive distribution being the core of Bayesian model evaluation or of model (epistemic) uncertainty. “On the other hand, a flat prior may have a major effect on marginalization.” Interesting sentence, as, from my viewpoint, using […]

logic (not logistic!) regression

A Bayesian Analysis paper by Aliaksandr Hubin, Geir Storvik, and Florian Frommlet on Bayesian logic regression was open for discussion. Here are some hasty notes I made during our group discussion in Paris Dauphine (and later turned into a discussion submitted to Bayesian Analysis): “Originally logic regression was introduced together with likelihood based model selection, […]

O’Bayes 19/1 [snapshots]

Although the tutorials of O’Bayes 2019 of yesterday were poorly attended, albeit them being great entries into objective Bayesian model choice, recent advances in MCMC methodology, and the multiple layers of BART, for which I have to blame myself for sticking the beginning of O’Bayes too closely to the end of BNP as only the […]

statistics with improper posteriors [or not]

Last December, Gunnar Taraldsen, Jarle Tufto, and Bo H. Lindqvist arXived a paper on using priors that lead to improper posteriors and [trying to] getting away with it! The central concept in their approach is Rényi’s generalisation of Kolmogorov’s version to define conditional probability distributions from infinite mass measures by conditioning on finite mass measurable […]

Computational Bayesian Statistics [book review]

This Cambridge University Press book by M. Antónia Amaral Turkman, Carlos Daniel Paulino, and Peter Müller is an enlarged translation of a set of lecture notes in Portuguese. (Warning: I have known Peter Müller from his PhD years in Purdue University and cannot pretend to perfect objectivity. For one thing, Peter once brought me frozen-solid […]