“Choosing the ranges has been criticized as introducing subjectivity; however, the key point is that the ranges are given quantitatively and should be justified” On arXiv, I came across a paper by physicists Dunstan, Crowne, and Drew, on computing the Bayes factor by linear regression. Paper that I found rather hard to read given that […]

## Search Results

## Easy computation of the Bayes Factor

August 21, 2021## Bayes factors revisited

March 22, 2021“Bayes factor analyses are highly sensitive to and crucially depend on prior assumptions about model parameters (…) Note that the dependency of Bayes factors on the prior goes beyond the dependency of the posterior on the prior. Importantly, for most interesting problems and models, Bayes factors cannot be computed analytically.” Daniel J. Schad, Bruno […]

## approximation of Bayes Factors via mixing

December 21, 2020A [new version of a] paper by Chenguang Dai and Jun S. Liu got my attention when it appeared on arXiv yesterday. Due to its title which reminded me of a solution to the normalising constant approximation that we proposed in the 2010 nested sampling evaluation paper we wrote with Nicolas. Recovering bridge sampling—mentioned by […]

## leave Bayes factors where they once belonged

February 19, 2019In the past weeks I have received and read several papers (and X validated entries)where the Bayes factor is used to compare priors. Which does not look right to me, not on the basis of my general dislike of Bayes factors!, but simply because this seems to clash with the (my?) concept of Bayesian model […]