Certifying Generic Identifiability with Matroids

A statistical model is identifiable if the map parameterizing the model is injective. This means that the parameters producing a probability distribution in the model can be uniquely determined from the distribution itself which is a critical property for meaningful data analysis. In this talk I'll discuss a new strategy for proving that discrete parameters are identifiable that uses algebraic matroids associated to statistical models. This technique allows us to avoid elimination and is also parallelizable. If time permits I'll also discuss a new extension of this technique which utilizes oriented matroids to prove identifiability results that the original matroid technique is unable to obtain.