#696 introduced several new abstractions for model composition in DynamicPPL.jl:
ReturnedModelWrapper
Wraps a model to treat it as a distribution over its return values instead of its parameters.
Example:
@model function demo()
x ~ Normal()
y = x + 1
return y
end
# ReturnedModelWrapper(demo()) represents distribution over yDistributional
Sampleable <: Distributional:
- Can be sampled from
- May not support logpdf computation
Extensions (To be implemented)
latent: (vis-a-vis returned)
- Would view model in terms of its parameters rather than returns
- Example: For above model, would give distribution over
x
- Another subtype of
Distributional that supports sampling and computing logp
- A high level user facing interface
to_distribution that would create proper probability distributions with models
- Could potentially work with both
returned and latent
Integration with NamedTupleVariate
Supporting NamedTupleVariate (#801) would give us a natural interface for to_distribution:
- Allowing model to be viewed as distributions over model parameters and their values.
- Notice
ProductNamedTupleDistribution is not sufficient, because it assumes independence
- Limitation is that not all DynamicPPL models can use this interface, as some require the more complex VarName to value mapping rather than simple NamedTuples
#696 introduced several new abstractions for model composition in DynamicPPL.jl:
ReturnedModelWrapperWraps a model to treat it as a distribution over its return values instead of its parameters.
Example:
DistributionalSampleable <: Distributional:Extensions (To be implemented)
latent: (vis-a-visreturned)xDistributionalthat supports sampling and computing logpto_distributionthat would create proper probability distributions with modelsreturnedandlatentIntegration with
NamedTupleVariateSupporting
NamedTupleVariate(#801) would give us a natural interface forto_distribution:ProductNamedTupleDistributionis not sufficient, because it assumes independence