He started his professional life as a probabilist and mathematical statistician with Bayesian leanings but became one of the world’s leading applied statisticians and a circumspect frequentist. In his words:

My own experience suggests that neither decision-makers nor their statisticians do in fact have prior probabilities. A large part of Bayesian statistics is about what you would do if you had a prior. For the rest, statisticians make up priors that are mathematically convenient or attractive. Once used, priors become familiar; therefore, they come to be accepted as ‘natural’ and are liable to be used again; such priors may eventually generate their own technical literature… Similarly, a large part of [frequentist] statistics is about what you would do if you had a model; and all of us spend enormous amounts of energy finding out what would happen if the data kept pouring in.

I have draft posts: one is about his book titled as Statistical Models: Theory and Practice and the other is about his article appeared in arXiv:stat not many months ago and now published in the American Statistician (TAS). In my opinion, both would help astronomers lowering the barrier of theoretical statistics, Bayesian and frequentist methods alike. I blame myself for delaying these posts. Carrying on one’s legacy, I believe, is easier while the person is alive.