Could someone please recommend reading on Bayesian statistics presented from a pure mathematical point of view? That is, works that start assuming a good knowledge of measure theoretic probability. The usual reference works for statisticians gloss over the fine details, but for me this just leads to more confusion!


I already know of the following question


[Statistics for mathematicians](https://mathoverflow.net/questions/31655/statistics-for-mathematicians)


and will follow up some of the suggestions there, but I am specifically looking for a treatment of the Bayesian approach, so if there are any recommendations that apply just to this I would appreciate hearing about them.



Bayesian statistics for pure mathematicians
Many hold that Bayesian statistics "from a purely mathematical point of view" is entirely coextensive with probability (however it is that you want to define its boundaries as a mathematical discipline). Nonetheless, if I interpret your request as being for a mathematically sophisticated and rigorous exposition on why the Bayesian approach is a worthy one, three book spring to mind.


1. Theory of Statistics by Mark Schervish
2. Bayes Theory by John Hartigan
3. The Bayesian Choice by Christian Robert


The first of these is a general graduate text in statistics, but the author gives uncommonly complete coverage of both Bayesian and frequentist methods.


The second is a smaller volume and, as I recall, is devoted to some of the more delicate issues surround finite versus countable additivity as relates to using probability distributions as priors in a Bayesian approach.


The final book is more general, but the style is more formal than the Bernardo and Smith book mentioned by PaPiro. (This is, in my experience, true of the style of French Bayesians :)


As I said, the distinctive elements of the Bayesian perspective are more philosophical than technical, but there are some technical areas that have received attention in the Bayesian community that may be of independent mathematical interest. One would be the role of so-called "improper" priors as mentioned above.


Another is the role of conditional distributions as a primitive rather than derived notion, leading to the idea of disintegration, as in this manuscript of [Pollard](http://www.stat.yale.edu/%7Epollard/Papers/chang-pollard.pdf).


Also, because of a keen interest in the application of Monte Carlo methods, Bayesian statisticians have to a lot of work on various aspects of computational methods for sampling from various distributions. Christian Robert is a prominent researcher in this area, and he has a [blog](https://xianblog.wordpress.com/). The current post happens to be about Bayesian foundations.


Finally, at the heart of a many arguments in favor of a Bayesian approach (early chapters in Bernardo and Smith and Robert are dedicated to it) are de Finetti type representation theorems, which sanction prior distributions via appeals to exchangeability. You can start with the wiki entry for [de Finetti theorems](https://en.wikipedia.org/wiki/De_Finetti%27s_theorem) and then look at the [work of Persi Diaconis](https://projecteuclid.org/journals/annals-of-probability/volume-8/issue-4/Finite-Exchangeable-Sequences/10.1214/aop/1176994663.full) on the topic. In this vein see also [Lauritzen's monograph](https://books.google.com/books?id=8Pzs_EujC28C&lpg=PA108&dq=lauritzen%25252520exchangeable&pg=PA108#v=onepage&q=lauritzen%20exchangeable&f=false), which (for me anyway) is the last word on the matter.


