Nonlinear Biomedical Physics

Department of Physics

Andrea Duggento

Andrea Duggento

I study Bayesian inference for non-linear dynamics. For given numerical data from a system (for example cardiovascular signals, neurons signalling etc.) we often want to test if our measurements are compatible with the theoretical model that we have built to explain such behaviour. The job of Bayesian machinery is to find the best parameter of the model that can mimic the real data; indirectly in this way we can evaluate the validity of the model itself.