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JuliaBUGS v0.10 #296

@sunxd3

Description

@sunxd3

The todo item is marked as complete does not reflect the main branch, but sunxd/v0.10 branch.

Issues to Address

Refactoring

  • Graph: more correctness tests
  • Gibbs: improve performance and make the implementation more clear
  • LogDensityProblems.logdensity_and_gradient probably can be implemented with DifferentiationInterface and thus simplify code related to ADgradientWrapper (we would still need to be compatible with the latter, but introduce a get_model function) (future)

More Tests

  • Properly test AD packages:(opt for better documentation)
    • currently the story is: use ReverseDiff (compiled) for performance when evaluating with graph, Mooncake or Enzyme with the generated source because the generated source mutates. It'll be better to make this clear by printing instructions after the model compilation.

Documentation

  • Update with the new Julia model macro syntax
  • Use DifferentiationInterface and Mooncake in documentation
    • There should be a section on picking AD backends
  • Source Generation: make clear it only work with parameter transformation and with Mooncake

Others

  • Remove archive folder and find alternative home for some of the idea here

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