A computer-implemented method for processing pulse oximeter data using a dynamic state space model (DSSM) to extract an estimated value for at least one physiological parameter not measured by said pulse oximeter, said method comprising: a) entering system and model parameters for a time t into a dynamic state space model to produce a first probability distribution function vector comprising state and model parameters for time t+n; b) using the first PDF vector and timed data obtained for time t+n from the sensor in a Bayesian statistical process to produce a second PDF vector for state and model parameters for time t+n; c) calculating probabilistic expectation values for the state and model parameters for time t+n from the second probability distribution function; and d) determining an estimated value for the at least one physiological parameter for time t+n from probabilistic expectation values for the state and/or model parameters for time t+nwherein: the state and model parameters for a time t entered into the dynamic state-space model in step a) are in the form of a probability distribution function produced from a sampling of expectation values calculated in step c) for an immediately preceding time t-n'; and n and n' are time intervals that may be the same of different characterized in that: the DSSM mathematically represents physiological processes that produce physiological parameters measured by said pulse oximeter and said at least one physiological parameter not measured by said pulse oximeter to produce a time dependent state representing a time dependent physiological state of the subject, the DSSM comprises at least one model parameter and/or state parameter representing at least one of total blood volume, stroke volume, vasomotor tone, and autonomous nervous system tone and the at least one physiological parameter not measured by said pulse oximeter is one of total blood volume, stroke volume, vasomotor tone, and autonomous nervous system tone. a) entering system and model parameters for a time t into a dynamic state space model to produce a first probability distribution function vector comprising state and model parameters for time t+n; b) using the first PDF vector and timed data obtained for time t+n from the sensor in a Bayesian statistical process to produce a second PDF vector for state and model parameters for time t+n; c) calculating probabilistic expectation values for the state and model parameters for time t+n from the second probability distribution function; and d) determining an estimated value for the at least one physiological parameter for time t+n from probabilistic expectation values for the state and/or model parameters for time t+nwherein: the state and model parameters for a time t entered into the dynamic state-space model in step a) are in the form of a probability distribution function produced from a sampling of expectation values calculated in step c) for an immediately preceding time t-n'; and n and n' are time intervals that may be the same of different characterized in that: the DSSM mathematically represents physiological processes that produce physiological parameters measured by said pulse oximeter and said at least one physiological parameter not measured by said pulse oximeter to produce a time dependent state representing a time dependent physiological state of the subject, the DSSM comprises at least one model parameter and/or state parameter representing at least one of total blood volume, stroke volume, vasomotor tone, and autonomous nervous system tone and the at least one physiological parameter not measured by said pulse oximeter is one of total blood volume, stroke volume, vasomotor tone, and autonomous nervous system tone. the state and model parameters for a time t entered into the dynamic state-space model in step a) are in the form of a probability distribution function produced from a sampling of expectation values calculated in step c) for an immediately preceding time t-n'; and n and n' are time intervals that may be the same of different characterized in that: the DSSM mathematically represents physiological processes that produce physiological parameters measured by said pulse oximeter and said at least one physiological parameter not measured by said pulse oximeter to produce a time dependent state representing a time dependent physiological state of the subject, the DSSM comprises at least one model parameter and/or state parameter representing at least one of total blood volume, stroke volume, vasomotor tone, and autonomous nervous system tone and the at least one physiological parameter not measured by said pulse oximeter is one of total blood volume, stroke volume, vasomotor tone, and autonomous nervous system tone. the DSSM mathematically represents physiological processes that produce physiological parameters measured by said pulse oximeter and said at least one physiological parameter not measured by said pulse oximeter to produce a time dependent state representing a time dependent physiological state of the subject, the DSSM comprises at least one model parameter and/or state parameter representing at least one of total blood volume, stroke volume, vasomotor tone, and autonomous nervous system tone and the at least one physiological parameter not measured by said pulse oximeter is one of total blood volume, stroke volume, vasomotor tone, and autonomous nervous system tone.