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#53 Bayesian Stats for the Behavioral & Neural Sciences, with Todd Hudson

Learning Bayesian Statistics

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The Transition From the Transparent to the Complex

When you're measuring a temperature, when you're measuring the length, these are transparent in the sense that the measurement equation is invisible to you. But then when you move to something more complex, where there is a model comparis, and it's more complicated, like the, like accidental decay, right? So now you have error data, but you also have an amplitude. And if they're conflating those two, then when they get to a a more complicated example, then it's hard too. It's hard to make that transition unless it's pointed out, ah, clearly.

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