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Consider a (stationary) autoregressive process, say of order 2,
for some white noise with variance . Here is a code to generate such a process,
> phi1=.5
> phi2=-.4
> sigma=1.5
> set.seed(1)
> n=240
> WN=rnorm(n,sd=sigma)
> ...

The Gaussian vector is extremely interesting since it remains Gaussian when conditioning. More precisely, if is a Gaussian random vector, then the conditional distribution of is also Gaussian. Further, it is possible to derive explicitly the cova...