# Bayesian Inference in Measure Theory

What's the deal. How does this work, or can you point me to some references? I tried $\mu(A|B) = \mu(A \cap B) / \mu(B)$ and got stuck on $\mu(B) = 0$.

Edit: Sorry for being lazy. My background is the basics of measure theory (working on it): measurable spaces, measurable functions, Lebesgue integral, that's about it so far. I haven't yet learned much about measure theory and probability. I am mainly just curious if there is a "formula" for Bayes' rule in measure theory? And interested in anything relevant.

One motivation is we often model a game in economics by have a finite set of states of the world with a prior distribution, then we learn that the true state is in some subset and update based on Bayes rule. I haven't seen how to model this with an infinite state space (I can only think of special cases where it would work).

Thanks!

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You can't condition on measure zero sets, it just doesn't make sense. Related question – icurays1 Jan 25 '13 at 21:38
I don't get it? You want a measure theoretc treatment of Bayesian statistics? – Michael Greinecker Jan 25 '13 at 21:44
@usul I fyour interest is probabilitisc conditioning, the most comprehensive book is probably Conditional Measures and Applications by M.M. Rao. A treatment of Bayesian statistics can be found here. None of these is for the beginner though. – Michael Greinecker Jan 25 '13 at 22:40
@MichaelGreinecker To send somebody to that reference for "a treatment of Bayesian statistics"? Honestly, this is rather flabbergasting. – Did Jan 26 '13 at 3:20
@usul Can you specfy what your background is and what you want to learn this for? Then I might give you a more tailormade reference. – Michael Greinecker Jan 26 '13 at 10:13

## 1 Answer

A classic book on Bayesian statistics, that makes (modest) use of measure theory is Optimal Statistical Decisions by Morris DeGroot.

Of course, Bayes rule holds even in the framework of measure theoretic probability. But for more general treatments of probabilistic conditioning, there is the very abstract framework for conditional expectations due to Kolmogorov based on the Radon-Nikodym theorem. Since the probability of an event equals the expectation of its indicator function, one can use this framework to treat conditional probabilities. More concrete, but less general, is working with regular conditional probabilities. Something these approaches are not going to help you with is conditioning on probability zero events, they do not matter in classical probability theory and they do not matter in Bayesian statistics.

Probability zero events do matter a lot in game theory, where the show up as off-equilibrium-beliefs. But the theory of refinements for Bayesian games with infinite type spaces is not yet satisfactory. For simple Bayesian equilibrium, the ability to build expectations ex ante is enough. The classical treatment of the issue can be found in the 1985 paper Distributional strategies for games with incomplete information by Milgrom and Roberts (in Mathematics of OR). The paper makes great use of the theory of weak convergence, which is quite important in mathematical game theory.

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Thanks for the help! – usul Jan 28 '13 at 15:22