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Our sample consists of 56 spectroscopically confirmed quasar light curves from the seventh data release of the Sloan Digital Sky Survey (Schneider et al. 2010). These quasars range in redshift from $z = 0.19$ to $z = 3.83$ and in luminosity from $L = 10^{15} L_{\odot}$ to $L = 10^{20} L_{\odot}$. %  These samples were taken from the Southern Equitorial Stripe known as Stripe 82. Stripe 82 is a $275^{2}$ degree area of sky with repeated sampling centered on the celestial equator. It reaches 2 magnitudes deeper than SDSS single pass data going as deep as magnitude 23.5 in the r-band for galaxies with median seeing of 1.1'' (Annis et al. 2014).  These samples were chosen because they exist in the field of the Kepler K2 mission's campaign 8. K2 has a much shorter cadence than SDSS with observations approximately every 30 minutes. This increase in time sampling rate will allow us to probe far deeper into the short-term variability properties of these objects as compared to SDSS. %Talk more about Kepler  Each light curve contains photometric information from two bands (g and r) with as much as 10 years of data. The number of epochs of data range from 29 observations to 81 observations in all photometric bands with sampling intervals ranging from one day to two years. This inconsistent sampling will lead to issues with our analysis which will be discussed later. %  PSF magnitudes are calibrated using a set of standard stars (Ivezic et al 2007) to reduce the error in our data down to 1\%. We then convert these magnitudes to fluxes for our analysis and convert observed time sampling intervals to the rest frame of the quasar. %  We use asinh magnitudes (also referred to as "Luptitudes") for flux conversion (York et al. 2000) (Lupton, Gunn, \& Szalay 1999) as is standard for SDSS.   These samples were chosen because they exist in the field of the Kepler K2 mission's campaign 8. The Kepler space telescope's original purpose was planet finding, which required that it quickly and consistently take many exposures over long periods of time in order to look for small periodic dips in the light from potential planetary systems. After the failure of two out of the four reaction wheels, the telescope was limited in it's pointing to it's orbital plane. Due to it's limited mobility, the K2 project was started which involved having the telescope observe single regions of the sky for approximately 75 days at a time with observations every 30 minutes. (Howell et al. 2014). Fortunately, one of these regions happens to overlap with Stripe 82, which means short term time-series data will soon be available for these objects. This increase in time sampling rate will allow us to probe far deeper into the short-term variability properties of these objects as compared to SDSS.  %Talk more about Kepler  \makefig{https://www.authorea.com/users/3982/articles/112661/master/file/figures/K2S82FOV/K2S82FOV.png}{The positions of the 56 SDSS quasars (red) overlaid on the K2 campaign 8 field of view (blue) and the Stripe 82 region (green). The combined long term variability information from SDSS and short term variability information from Kepler will allow us to more tightly constrain out models in the future. }  \section{Continuous-Time Auto Regressive Moving Average} 
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  \section{The Kalman Filter}  AGN light curves only give us a picture of the effects that the underlying physical processes have on the AGN itself, but they don't directly tell us what those physical processes are or how they work. Futher more, observation error makes it even more difficult to determine their structure. To understand these processes, we need a method of inspecting the state of the system based on our observations that can also take into account our observation error. Using a Kalman filter together with our CARMA model, we can accurately model theses underlying physical processes and being analyzing the true structure of the system.  \section{Fitting CARMA Models}  \subsection{CARMA_Pack}  Computationally analyzing and fitting CARMA models requires a fairly sophisticated package. 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 data sets. For our analysis, we have decided to use a python library written in C++ called carma_pack (Kelly 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.    \section{Canonical Light Curves}