I’d like to do inference of a fairly long AR(1) process. This means there are lots of variables (the innovations of the process) and they are correlated so things start to slow down.
Is it possible to do inference on the other parameters and effectively ignore the posterior of the AR(1) innovations? Is that what is being discussed under “Conditional Inference” here in the Edward docs?
http://edwardlib.org/api/inference
Would this or any other techniques make a larger AR(1) inference possible? e.g. with 1000’s of innovations but only looking at the posterior of the parameters?