[the]{} time domain to fit the data, and infers the model parameters using a Markov-chain Monte Carlo (MCMC) algorithm (see @Li2018c for details). Compared with methods that directly fit PSDs (e.g., @Vaughan2016), such an approach can cope with irregularly sampled light curves and also naturally take into account the sampling effects, such as spectral leakage and aliasing. In addition, the framework calculates the Bayesian evidence and hence the Bayes factor that allows us [to]{} perform model comparison. The shortcoming of [this]{} approach is that the (inverse) Fourier transform and MCMC sampling are highly computationally expensive for large numbers of data points. To save computation time, we bin the light curve every 10 days, reducing the number of points from 1473 to 471. Such a binning manipulation degrades the maximum frequency that can be [obtained]{} from the data. Since we mainly concentrate on variations with time scales of years, this manipulation does not influence our main analysis results. [It is worth stressing that in fitting the light curve, we assume independent, Gaussian measurement noise. ]{} Here, [the]{} Bayes factor is defined to be the ratio of the posterior probabilities (@Sivia2006). For two models, say $M_1$ and $M_2$, with equal prior weight, 