Title: Time-Varying Parameter Estimation for Biological Models
Abstract: Many applications in the life sciences involve unknown system parameters that must be estimated using little to no prior information. In addition, these parameters may be time-varying and possibly subject to structural characteristics such as periodicity. We show how nonlinear Bayesian filtering techniques can be employed to estimate periodic, time-varying parameters, while naturally providing a measure of uncertainty in the estimation. Results are demonstrated using data from several biological applications, including cardiovascular dynamics and the spread of infectious diseases.