Detecting hidden states in stochastic dynamical systems

Rayan Succar, Alain Boldini, Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review


Inferring the number of states of a stochastic system from partial measurements is a fundamental problem in physics, for which methodological tools remain scarce. It is sometimes difficult to distinguish the stochastic dynamical states from measurements, deceiving us into incorrect models and flawed understanding of natural phenomena. Here, we propose a model-free statistical framework, grounded in network and control theory, to estimate the number of states of a stochastic system from perceptible dynamics. The framework extends previous techniques for deterministic systems, based on the rank of ancillary matrices. We show applications of our approach to a variety of physics domains, such as statistical mechanics, biophysics, physical chemistry, and epidemiology.

Original languageEnglish (US)
Article number013149
JournalPhysical Review Research
Issue number1
StatePublished - Jan 2024

ASJC Scopus subject areas

  • General Physics and Astronomy


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