Declarative Statistics
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.
Cite
@article{arxiv.1708.01829,
title = {Declarative Statistics},
author = {Roberto Rossi and Özgür Akgün and Steven Prestwich and S. Armagan Tarim},
journal= {arXiv preprint arXiv:1708.01829},
year = {2017}
}
Comments
The modeling framework and the examples used in this work are available at https://gwr3n.github.io/syat-choco/