We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set of polynomial basis functions representing the nonlinear force governing system oscillations. The strength and direction of coupling and noise intensity are simultaneously inferred from a univariate blood pressure signal. Our new inference technique does not require extensive global optimization, and it is applicable to a wide range of complex dynamical systems subject to noise.
First use of Bayesian inference to build a nonlinear stochastic model of the cardio-respiratory interaction in terms of polynomial basis functions, directly from a univariate blood pressure signal. The technique is widely applicable in dynamical modelling. RAE_import_type : Journal article RAE_uoa_type : Physics
