Work

Learning and inference in a nonequilibrium Ising model with hidden nodes

Year: 2013

Type: article

Abstract: We study inference and reconstruction of couplings in a partially observed kinetic Ising model. With hidden spins, calculating the likelihood of a sequence of observed spin configurations requires performing a trace over the configurations of the hidden ones. This, as we show, can be represented as a path integral. Using this representation, we demonstrate that systematic approximate inference and learning rules can be derived using dynamical mean-field theory. Although naive mean-field theory leads to an unstable learning rule, taking into account Gaussian corrections allows learning the couplings involving hidden nodes. It also improves learning of the couplings between the observed nodes compared to when hidden nodes are ignored.

Source: Physical Review E

DOI: 10.1103/physreve.87.022127

Authors: Benjamin Dunn, Yasser Roudi

Institutions: Norwegian University of Science and Technology, Kavli Institute for Systems Neuroscience, Nordic Institute for Theoretical Physics, Stockholm University, KTH Royal Institute of Technology

Language: English

Topic: Theoretical and Computational Physics

Subfield: Condensed Matter Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Universitetet i Oslo

OpenAlex ID: W1993595700

FWCI: 2.626

Cites: 40

Cited by: 46

Related to: 10