v0.41.1

Turing v0.41.1

Diff since v0.41.0

The ModeResult struct returned by maximum_a_posteriori and maximum_likelihood can now be wrapped in InitFromParams().
This makes it easier to use the parameters in downstream code, e.g. when specifying initial parameters for MCMC sampling.
For example:

@model function f()
    # ...
end
model = f()
opt_result = maximum_a_posteriori(model)
sample(model, NUTS(), 1000; initial_params=InitFromParams(opt_result))

If you need to access the dictionary of parameters, it is stored in opt_result.params but note that this field may change in future breaking releases as that Turing's optimisation interface is slated for overhaul in the near future.

Merged pull requests:

• CompatHelper: add new compat entry for DynamicPPL at version 0.38 for package test, (keep existing compat) (#2701) (@github-actions[bot])
• Move external sampler interface to AbstractMCMC (#2704) (@penelopeysm)
• Skip Mooncake on 1.12 (#2705) (@penelopeysm)
• Test on 1.12 (#2707) (@penelopeysm)
• Include parameter dictionary in optimisation return value (#2710) (@penelopeysm)

Closed issues:

• Better support for AbstractSampler (#2011)
• "failed to find valid initial parameters" without the use of truncated (#2476)
• Unify src/mcmc/Inference.jl methods (#2631)
• Could not run even the sample code for Gaussian Mixture Models or Infinite Mixture Models (#2690)
• type unstable code in Gibbs fails with Enzyme (#2706)