  We provide a new approach to measure power spectra and reconstruct time
series in active galactic nuclei (AGNs) based on the fact that the Fourier
transform of AGN stochastic variations is a series of complex Gaussian random
variables. The approach parameterizes a stochastic series in frequency domain
and transforms it back to time domain to fit the observed data. The parameters
and their uncertainties are derived in a Bayesian framework, which also allows
us to compare the relative merits of different power spectral density models.
The well-developed fast Fourier transform algorithm together with parallel
computation enable an acceptable time complexity for the approach.
