This work develops application techniques for stochastic modelling of Active Galactic Nuclei (AGN) variability as a probe of accretion disk physics. Stochastic models, specifically Continuous Auto-Regressive Moving Average (CARMA) models, characterize lightcurves by estimating delay timescales that describe movements away from and toward equilibrium (mean flux) as well as an amplitude and frequency of intrinsic perturbations to the AGN flux. We begin this tutorial by reviewing discrete auto-regressive (AR) and moving-average (MA) processes, we bridge these components to their continuous analogs, and lastly we investigate the significance of timescales from direct stochastic modelling of a lightcurve projected in power spectrum (PSD) and structure function (SF) space. We determine that higher order CARMA models, for example the Damped Harmonic Oscillator (DHO or CARMA(2,1)) are more sensitive to deviations from a single-slope power-law description of AGN variability; unlike Damped Random Walks (DRW or CAR(1)) where the PSD slope is fixed, the DHO slope is not. Higher complexity stochastic models than the DRW capture additional covariance in data and output additional characteristic timescales that probe the driving mechanisms of variability.
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