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Inferring hidden states in Langevin dynamics on large networks: Average case performance

Research output: Contribution to journalArticle

Original languageEnglish
Article number10.1103/PhysRevE.95.012122
Pages (from-to)1-20
Number of pages20
JournalPHYSICAL REVIEW E
Volume95
Issue number1
DOIs
Publication statusPublished - 13 Jan 2017

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Abstract

We present average performance results for dynamical inference problems in large networks, where a set of nodes is hidden while the time trajectories of the others are observed. Examples of this scenario can occur in signal transduction and gene regulation networks. We focus on the linear
stochastic dynamics of continuous variables interacting via random Gaussian couplings of generic
symmetry. We analyze the inference error, given by the variance of the posterior distribution over
hidden paths, in the thermodynamic limit and as a function of the system parameters and the ratio
α between the number of hidden and observed nodes. By applying Kalman filter recursions we
find that the posterior dynamics is governed by an “effective” drift that incorporates the effect of
the observations. We present two approaches for characterizing the posterior variance that allow
us to tackle, respectively, equilibrium and non-equilibrium dynamics. The first appeals to Random
Matrix Theory and reveals average spectral properties of the inference error and typical posterior
relaxation times, the second is based on dynamical functionals and yields the inference error as the
solution of an algebraic equation.

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