SiGMoiD: A super-statistical generative model for binary data.

ABSTRACT: In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers' identification of constraints and are computationally expensive to infer when the number of variables is large (N~100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same 'bath' whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints,

SUBMITTER: Zhao X 

PROVIDER: S-EPMC8372922 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature