Uniform bounds on rate of merging for Bayesian learners

Question

Update. Cross posted at Cross Validated.

In a well-known paper, Blackwell & Dubins (1962) show that the posterior probabilities of two Bayesian agents, whose priors agree on events of measure $0$, will become arbitrarily close to each other under an increasing stream of information.

Mathematically, the result is as follows. Let $(\Omega, \mathcal{F}, \{\mathcal{F}_n\}, Q)$ be a filtered probability space with $\mathcal{F}_n \uparrow \mathcal{F}$. Let $P$ be a probability on $(\Omega, \mathcal{F})$ with $Q \ll P$. Then, $$d(P^n, Q^n): = \sup_{A \in \mathcal{F}}|P(A \mid \mathcal{F}_n) - Q(A \mid \mathcal{F}_n)| \to 0 \text{ a.s. $Q$ as } n \to \infty.$$ We say that $P$ and $Q$ strongly merge.

In a more recent and also very influential paper, Kalai & Lehrer (1994) introduce the notion of weak merging. The definition is as above, except the $\sup$ is taken over finite horizon events; tail events are ignored: $$w(P^n, Q^n) : = \sup_{A \in \mathcal{F}_{n+1}}|P(A \mid \mathcal{F}_n) - Q(A \mid \mathcal{F}_n)| \to 0 \text{ a.s. $Q$ as } n \to \infty.$$

For weak merging it is possible to find uniform bounds on the rate of convergence (Fudenberg & Levine, 1992; Sorin, 1999). I am wondering if there are any results in this direction for strong merging.

Answer 1

This paper by Acemoglu, Chernozhukov and Yildiz (2016) and the references therein may be of interest.

The results they derive are in a much more limited environment, but I think they still gesture toward the direction you're looking. Otherwise, their literature review should also prove useful.