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\subsection{ARMA}  %**Talk about using white noise to drive the process  A process $X_t, t \elementof \integer$  %**Talk about using white noise to drive the process  %define stationary  %are lightcurves stationary?  %**mention light curves as the time-series we're talking about  Analysis of astronomical time-dependent data sets requires methods of quantifying and parameterizing the properties of the observed process in order to learn about the underlying physical processes driving them. The most common method of parameterization of light curve variability currently is to compute the structure function. The structure function is useful because it allows us to relate the variation in brightness to the period of time over which we observe the change. The structure function is characterized by a power law with a slope and y-intercept as free parameters. While a useful tool, it lacks the sophistication required to probe the complex behavior of AGN which require far more parameters to effectively model due to their complicated structure. Instead, we look to other tools commonly used in time-series analysis.  
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\subsection{CARMA\_Pack}  Computationally analyzing and fitting CARMA models requires a fairly sophisticated package. %why?   %reasons: Many components (Kfilter, Metropolis-hastings, math)  % MCMC needs to run for many steps and needs to converge quickly  Though many scripting and data analysis languages offer libraries for ARMA analysis such as R and Matlab, few offer tools for the analysis of continuous-time data sets. For our analysis, we have decided to use a python library written in C++ called carma\_pack. Carma\_pack was originally written for the purpose of analyzing astronomical time-series, specifically, AGN. It has been used successfully to derive the parametric structure of light curves of AGN taken by the Kepler satellite, and has shown to be a useful tool in the analysis of SDSS light curves (Kelly et. al 2014). This package allows us to easily and conveniently fit CARMA process to light curves, determine the optimal model orders, and perform basic stochastic analysis of our best fit models. Carma\_pack fits CARMA processes to light curves using the Metropolis-Hastings Markov-chain Monte-Carlo method (citation needed) with a Kalman filter providing the likelihood estimations for the proposals. \subsection{Model Orders}