Abstract

This workshop brings together both theoreticians and practitioners from broadly different fields to advance algebraic statistical methodology to data science. While linear algebra underlies classical statistics, in algebraic statistics, the aim is to explore the applicability of nonlinear algebra---which extends algebraic theory beyond linear algebra---to statistical theory. In particular, our goal is to advance the computational feasibility of algebraic statistics. Our focus is specifically on direct applicability of algebraic statistical theory to real datasets. Additionally, this workshop explores other statistical settings that are also algebraic in nature and that find practical applications in various different fields, where nonlinear algebra may also be applied.