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#61 Why we still use non-Bayesian methods, with EJ Wagenmakers

Learning Bayesian Statistics

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Using Cross-Validation in Model Comparison Algorithms

The appeal of these alternative methods is also exactly its bot kind of both their strength and their weakness, because these alternative methods are usually not so sensitive to prior distribution. You're losing ocom's razor when you don't bring the prior into play. The more narrow the predictions, the more parsimonious the model. So if you ignore the prior, the price you pay as you move away from a implementation of okam's razor. And that's when your priors become really important.

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