ots + \theta_Q \epsilon_{t-Q}
+ \epsilon_t,
\]</span>
with the noise term <span class="math inline">\(\epsilon_t\)</span> for outcome <span class="math inline">\(y_t\)</span> modeled as
normal,
<span class="math display">\[
\epsilon_t \sim \mathsf{normal}(0,\sigma).
\]</span>
In a proper Bayesian model, the parameters <span class="math inline">\(\mu\)</span>, <span class="math inline">\(\theta\)</span>, and
<span class="math inline">\(\sigma\)</span> must all be given priors.</p>
<div id="ma2-example" class