An autoregressive process can be described by the equation

\[y_t = c + \phi y_{t-1} + \epsilon.\]

The parameter \(c\) is some baseline, \(\phi\) if between -1 and 1, and \(\epsilon\) is some white noise process. If we consult the Wikipedia article on such process we find that there it is fairly trivial to calculate the unknown parameter \(\phi\) in a frequentist setting. Googling about for a Bayesian introduction didn’t turn up anything particularly helpful, so here I try to plug that gap.

For any Bayesian method we need to decompose our problem into three parts; likelihood, prior and posterior distribution. For simplicity we will be setting \(c=0\).

For the likelihood we can see that each observation \(y_i\) is normally distributed around \(\phi y_{i-1}\) with variance equal to that of the white noise process \(\epsilon\)

\[p(y_i | y_1, \ldots , y_n , \sigma _\epsilon ^2) \propto \frac{1}{\sigma _\epsilon} \exp \left( \frac{-(y_i - \phi y_{i-1})^2}{2 \sigma _\epsilon ^2} \right),\]

now the likelihood is just this density multiplied across all the data.

Now for the prior. Like any Gaussian inference problem it is a smart choice to use a Gaussian prior on \(\phi\) so that we get a conjugate prior. But there is a hard limit on the values of the parameter in question \(-1 < \phi < 1\), therefore we must use the truncated normal distribution.

\[(\phi | \mu _0 , \sigma _0 ^2) = \frac{\exp \left( - \frac{ (\phi - \mu_0) ^2}{2 \sigma _0 ^2} \right)}{\sqrt{2 \pi} \sigma_0 \left(\Phi \left( \frac{b-\mu_0}{\sigma _0} \right) - \Phi \left( \frac{a-\mu_0}{\sigma _0} \right) \right) }\]

the values of \(a , b\) set the limits of the truncation, so in our case they will be \(-1, 1\) respectively.

So lets combine both the likelihood and the prior to get our posterior distribution for \(\phi\). Due to the conjugacy of the prior, we know that the posterior is also going to be a truncated normal distribution.

\[p( \phi | y_1 , \ldots , y_n, \mu _0 , \sigma _0 ^2, \sigma _{\epsilon} ^2 ) = \text{Truncated-Normal} ( \mu \sigma ^{2 } , \sigma ^2 )\] \[\mu = \frac{\sum _i y_i y_{i-1}}{\sigma _\epsilon ^2} + \frac{\mu _0}{ \sigma _0 ^2}\] \[\sigma ^{2 } = \left( \frac{\sum_i y_i ^2}{\sigma _\epsilon ^2} + \frac{1}{\sigma _0^2} \right)^{-1}\]

Now these are simple enough to implement in a few lines of R and with such a simple model I’ll leave that as an exercise to the reader.