## Fiducial model averaging of Bayesian models and of frequentist models

D. R. Bickel, “A note on fiducial model averaging as an alternative to checking Bayesian and frequentist models,” *Communications in Statistics – Theory and Methods* **47**, 3125-3137 (2018). Full article | 2015 preprint

## Pre-data insights update priors via Bayes’s theorem

D. R. Bickel, “Bayesian revision of a prior given prior-data conflict, expert opinion, or a similar insight: A large-deviation approach,” *Statistics* **52**, 552-570 (2018). Full text | 2015 preprint | Simple explanation

## How to make decisions using somewhat reliable posterior distributions

D. R. Bickel, “Departing from Bayesian inference toward minimaxity to the extent that the posterior distribution is unreliable,” Working Paper, University of Ottawa, <hal-01673783>** **https://hal.archives-ouvertes.fr/hal-01673783 (2017). 2017 preprint

## Do models have probabilities or just possibilities?

Andrew says: David:I don’t think it makes sense to talk of the probability of a model. See this paper with Shalizi for much discussion of this point.

David Bickel says: If models do not have probabilities, perhaps they have possibilities in the sense of possibility theory. For example, the possibility of a model might be a function of its adequacy according to a model checking procedure: Appendix B of https://goo.gl/5s7bS3

## “Can You Change Your Bayesian Prior?”

Sometimes. A subjective Bayesian encountering completely unexpected data changes the prior:In the philosophy literature, that has been compared to changing the premises of a deductive argument. It has been argued that just as one may revise a premise without abandoning deductive logic as a tool, one may revise a prior without abandoning Bayesian updating as a tool.