Discrepancy

Marcel van Oijen ()

Chapter Chapter 13 in Bayesian Compendium, 2020, pp 89-92 from Springer

Abstract: Abstract We have seen that if we have a probability distribution for our model’s parameters, then we can sample from that distribution to see how parameter uncertainty translates into predictive uncertainty. And if we get new data, then we can use Bayes’ Theorem to update the parameter distribution and thereby reduce our predictive uncertainty. But a more difficult problem is that of uncertainty about model structure. We know that all models are wrong, but not how wrong they are.

Date: 2020
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DOI: 10.1007/978-3-030-55897-0_13

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