Estimating free parameters in stochastic oscillatory models using a weighted cost function
Abstract
In this study, we estimate parameter values in stochastic oscillatory systems by developing a cost function. Our cost function incorporates power spectral density, analytic signal, and position crossings, each weighted to capture distinct characteristics of the oscillatory system such as amplitude, frequency, and shape. By minimizing this cost function, we estimate parameter values in a stochastic biophysical model for auditory mechanics. Furthermore, we develop a procedure to align the phases of two time series, allowing us to compare stochastic phase evolution of two time series. As a broader application of our procedure, we establish a framework for fitting stochastic oscillatory systems and comparing stochastic time series in other systems.
Fields of Study & Subjects
Stochastic processes and financial applications
Publication details
- Journal / source
- Physical Review Research
- Publisher
- American Physical Society (APS)
- Volume
- 8
- Issue
- 1
- Publication date
- 20 February 2026
- Language
- EN