For real datasets, where it's impossible to know the true inverse covariance, what are the methods of evaluating your inverse covariance estimator?
If the number of features is long enough, randomly separate them into a train and test set. (Followup question: Do you do your own inverse covariance on both of them and compare to see if they're consistent? Or do you try to truly invert the one with more data? In that case, don't you still risk errors as long as p << n?
Calculate the log-likelihood of held out data. I'm wondering for this, what is the common practice for holding out data? For time series: do you hold out contiguous swaths, or randomly pick time points? For features that are not that associated (temperature, pressure, gene experession, etc) do you just randomly pick, or do you try to equally account for categorical, real, integer types of data?