MMP.7:10 - Architectural Rationale

The observation procedure connects the subject to the data used in inference. Keeping that connection explicit makes deterministic coarsening, random selection and shared uncertainty instances of one construction. It also separates modeling choices from the later choice of an inference algorithm.

The factors are chosen for the procedure and question. Their order as a probability factorization does not establish a causal direction in the represented world. Several factorizations can describe one joint law; the subject account and intervention question determine any causal interpretation.

This method uses C.16’s measurement and resolvability work while supplying the probability operations those patterns leave to statistical modeling. Its examples require different transformations: conditioning after selection, joint elimination and tail integration. The transferable operation survives replacing the logged activity, sensor or timed event.