Declarative Statistics

TL;DR

Declarative statistics introduces a new suite of modeling tools using statistical constraints for diverse statistical analysis tasks, emphasizing declarative programming and novel constraints.

Contribution

It presents a comprehensive set of novel statistical constraints and decompositions, enabling flexible and declarative statistical modeling.

Findings

New statistical constraints and decompositions introduced

Framework applicable to classical statistics problems

Contrasts with traditional statistical practices

Abstract

In this work we introduce declarative statistics, a suite of declarative modelling tools for statistical analysis. Statistical constraints represent the key building block of declarative statistics. First, we introduce a range of relevant counting and matrix constraints and associated decompositions, some of which novel, that are instrumental in the design of statistical constraints. Second, we introduce a selection of novel statistical constraints and associated decompositions, which constitute a self-contained toolbox that can be used to tackle a wide range of problems typically encountered by statisticians. Finally, we deploy these statistical constraints to a wide range of application areas drawn from classical statistics and we contrast our framework against established practices.