CMP.9:10 - Architectural Rationale

Sampling and estimation share the construction of an output law but return different things. An estimator can correct biased sampling without turning the retained draws into unweighted samples from the target. A transition can preserve a distribution without supplying independent draws. Keeping these results distinct makes their composition with learning, search and modeling reliable.

The general method derives probability statements from effective operations. Probability modeling supplies a target where needed; algorithmics supplies how to obtain, transform and use finite draws under resource limits.