Inverse Ising techniques to infer underlying mechanisms from data

• Published in: arXiv.org
• Main Authors: Hong-Li, Zeng, Aurell, Erik
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 09.02.2020
• Subjects: Boltzmann distribution; Homology; Ising model; Mathematical models; Normalizing; Parameters; Partitions (mathematics); Proteins

As a problem in data science the inverse Ising (or Potts) problem is to infer the parameters of a Gibbs-Boltzmann distributions of an Ising (or Potts) model from samples drawn from that distribution. The algorithmic and computational interest stems from the fact that this inference task cannot be do...