Bayesian updating of beliefs. Mathematics > Statistics Theory.



Bayesian updating of beliefs

Bayesian updating of beliefs

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Bayesian updating of beliefs

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Bayesian updating of beliefs

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4 Comments

  1. More importantly, there are settings where the parameter of interest does not directly index a family of density functions and thus the Bayesian approach to learning about such parameters is currently regarded as problematic. The only difference is that the posterior predictive distribution uses the updated values of the hyperparameters applying the Bayesian update rules given in the conjugate prior article , while the prior predictive distribution uses the values of the hyperparameters that appear in the prior distribution. In some instances, frequentist statistics can work around this problem.

  2. ST ; Methodology stat. Importantly, the procedure coincides with Bayesian updating when a true likelihood is known, yet provides coherent subjective inference in much more general settings.

  3. Our proposed framework uses loss-functions to connect information in the data to functionals of interest.

  4. Inference over exclusive and exhaustive possibilities[ edit ] If evidence is simultaneously used to update belief over a set of exclusive and exhaustive propositions, Bayesian inference may be thought of as acting on this belief distribution as a whole.

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