Is there a procedure in Bayesian inference to determine how much new information in the future invalidates your model?
Say I have some kind of time-series data, and I make an inference from it up to the current time. If the data is costly to get in the future, would I have a way of determining when cost of increasing error exceeds the cost of getting the new data and updating my inference?
Generally speaking, for this you need a meta-model, that is, a model of how your model will change (e.g. become outdated) with the arrival of new information. Plus, if you want to compare costs, you need a loss function which will tell you how costly the errors of your model are.
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