  We present a Bayesian dynamical inference method for characterizing
cardiorespiratory (CR) dynamics in humans by inverse modelling from blood
pressure time-series data. This new method is applicable to a broad range of
stochastic dynamical models, and can be implemented without severe
computational demands. A simple nonlinear dynamical model is found that
describes a measured blood pressure time-series in the primary frequency band
of the CR dynamics. The accuracy of the method is investigated using surrogate
data with parameters close to the parameters inferred in the experiment. The
connection of the inferred model to a well-known beat-to-beat model of the
baroreflex is discussed.
