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MrMind comments on Open thread, Jul. 18 - Jul. 24, 2016 - Less Wrong Discussion
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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?
Unfortunately to pull this off you need to look closely to both your model and the model of the error, there's no general method AFAIK.