In more strictly mathematical terms, having a "normative" update rule (Bayes' rule) doesn't tell you what topology of latent variables the generative model "ought" to have, only how to link new information into a preexisting generative model.
Using the KL divergence of the posterior predictive distribution as a target to optimize does a bit better, but still isn't a "solution".
>Seriously, what doeos "topology of latent variables" even mean?
The simple answer is: the graph topology of the resulting program traces, equivalent to the topology of a graphical model sampled from a distribution over graphical models. The complicated answer is: the Scott topology of the program-trace space.
Using the KL divergence of the posterior predictive distribution as a target to optimize does a bit better, but still isn't a "solution".