Let me express my approach in a slightly different way.

Let's say I have a sample of some numbers and I'm interested in the properties of future numbers coming out of the same underlying process.

The simplest approach (say, Level 1) is to have a point estimate. Here is my expected value for the future numbers.

But wait! There is uncertainty. At Level 2 I specify a distribution, say, a Gaussian with a particular mean and standard deviation (note that it implies e.g. very specific "hard" probabilities of seeing particulate future numbers).

But wait! There is more uncertainty! At Level 3 I specify that the mean of that Gaussian is actually uncertain, too, and has a standard error -- in effect it is a distribution (meaning your "hard" probabilities from the previous level just became "soft"). And the variance is uncertain, too, and has parameters of its own.

But wait! You can dive deeper and find yet more turtles down there.