Question: Asymptotics take on a different meaning in the Bayesian estimation context, since parameters do not converge to a population quantity. Nonetheless, in a Bayesian estimation

Asymptotics take on a different meaning in the Bayesian estimation context, since parameters do not “converge” to a population quantity. Nonetheless, in a Bayesian estimation setting, as the sample size increases, the likelihood function will dominate the posterior density. What does this imply about the Bayesian “estimator” when this occurs?

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