Matrix Product Belief Propagation for reweighted stochastic dynamics over graphs

Abstract

Stochastic processes on graphs can describe a great variety of phenomena ranging from neural activity to epidemic spreading. While many existing methods can accurately describe typical realizations of such processes, computing properties of extremely rare events is a hard task. Particularly so in the case of recurrent models, in which variables may return to a previously visited state. Here, we build on the matrix product cavity method, extending it fundamentally in two directions: first, we show how it can be applied to Markov processes biased by arbitrary reweighting factors that concentrate most of the probability mass on rare events. Second, we introduce an efficient scheme to reduce the computational cost of a single node update from exponential to polynomial in the node degree. Two applications are considered: inference of infection probabilities from sparse observations within the SIRS epidemic model, and the computation of both typical observables and large deviations of several kinetic Ising models.

22 pages, 7 figures, 1 table, appendix

References in corpus (5)

• The density-matrix renormalization group in the age of matrix product states
• Matrix product states represent ground states faithfully
• A message passing approach for general epidemic models
• Mean Field Theory For Non-Equilibrium Network Reconstruction
• Identification of Patient Zero in Static and Temporal Networks - Robustness and Limitations

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• A short review on the maximum clique problem algorithms with classical, AI, and quantum methods
• Nonequilibrium steady-state dynamics of Markov processes on graphs