# The Design of Global Correlation Quantifiers and Continuous Notions of Statistical Sufficiency

arXiv: https://arxiv.org/abs/1907.06992 · HTML: https://mlsanity.com/paper/1907.06992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Inferential Moments of Uncertain Multivariable Systems](https://mlsanity.com/paper/2305.01841/) (2023)

## Abstract (arXiv)

Using first principles from inference, we design a set of functionals for the purposes of textitranking joint probability distributions with respect to their correlations. Starting with a general functional, we impose its desired behaviour through the textitPrinciple of Constant Correlations (PCC), which constrains the correlation functional to behave in a consistent way under statistically independent inferential transformations. The PCC guides us in choosing the appropriate design criteria for constructing the desired functionals. Since the derivations depend on a choice of partitioning the variable space into n disjoint subspaces, the general functional we design is the n-partite information (NPI), of which the textittotal correlation and textitmutual information are special cases. Thus, these functionals are found to be uniquely capable of determining whether a certain class of inferential transformations, ρxrightarrow*ρ', preserve, destroy or create correlations. This provides conceptual clarity by ruling out other possible global correlation quantifiers. Finally, the derivation and results allow us to quantify non-binary notions of statistical sufficency. Our results express what percentage of the correlations are preserved under a given inferential transformation or variable mapping.