In my previous blog post I outlined the basic AR(1) model and the necessary maths needed to infer the unknown parameter $$\phi$$. In this post I will outline some basic Julia code to build a MCMC sampler for such a model to infer the unknown parameter $$\phi$$.

Firstly, we need to simulate some data. From the previous post we know that the data $$y$$ comes simply from the previous value, plus some fixed noise. In Julia this is simply writing a for loop and using the Distributions package to sample some white noise.

For 1000 data points with $$\phi=0.5$$ such a process looks like: Pretty much looks like a random walk around 0 as expected.

Now to compute the statistics for the posterior distribution we need the sum of squares and the lagged sum of squares ( [see here] (https://dm13450.github.io/2017/06/09/Bayesian-Auto-Process.html)). Then using the Distributions package again we can sample from a truncated normal distribution. We have used a prior distribution of a truncated normal distribution with 0 mean and a standard deviation of 5. So we can see that the posterior distribution for $$\phi$$ is close to the true value of 0.5, so it looks like our algorithm is working.

Although its just a toy model in these posts I have shown how to calculate the posterior for an autoregressive process and how to draw from such a distribution using Julia. Next stop, include more parameters and see how flexible autoregressive models can be.