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What are the limitations of the bayesian inference?

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Let me answer this somewhat indirectly. Bayesian inference is wonderful, if: You do not screw up with the priors in the modeling phase. Picking priors is very tricky. read more

Bayesian inference is wonderful, if: You do not screw up with the priors in the modeling phase. Picking priors is very tricky. You need to think of what properties you want your model to satisfy, how many parameters you will introduce with one prior versus another, the information that priors convey. read more

Frequentist refers to the evaluation of statistical procedures but it doesn’t really say where the estimate or prediction comes from. Rather, I’d say that the Bayesian prediction approach succeeds by adding model structure and prior information. The advantages of Bayesian inference include: 1. Including good information should improve prediction, 2. read more

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