v0.37.4

DynamicPPL v0.37.4

Diff since v0.37.3

An extension for MarginalLogDensities.jl has been added.

Loading DynamicPPL and MarginalLogDensities now provides the DynamicPPL.marginalize function to marginalise out variables from a model.
This is useful for averaging out random effects or nuisance parameters while improving inference on fixed effects/parameters of interest.
The marginalize function returns a MarginalLogDensities.MarginalLogDensity, a function-like callable struct that returns the approximate log-density of a subset of the parameters after integrating out the rest of them.
By default, this uses the Laplace approximation and sparse AD, making the marginalisation computationally very efficient.
Note that the Laplace approximation relies on the model being differentiable with respect to the marginalised variables, and that their posteriors are unimodal and approximately Gaussian.

Please see the MarginalLogDensities documentation and the new Marginalisation section of the DynamicPPL documentation for further information.

Merged pull requests:

• pretty tables 3 (#1026) (@penelopeysm)
• MarginalLogDensities extension (#1036) (@penelopeysm)
• pin JET to <= 0.10.6 (#1046) (@penelopeysm)

Closed issues:

• Enzyme doesn't like accumulators (#947)
• JET 1.12 tests are failing (#1040)