Return to Answer

...What should we conclude from all this? The important thing is to understand that frequentist and Bayesian methods are answering dierent questions. To combine prior beliefs with data in a principled way, use bayesian inference. To construct procedures with guaranteed long run performance, such as condence intervals, use frequentist methods... (p217)

...What should we conclude from all this? The important thing is to understand that frequentist and Bayesian methods are answering different questions. To combine prior beliefs with data in a principled way, use Bayesian inference. To construct procedures with guaranteed long run performance, such as confidence intervals, use frequentist methods... (p217)

This is a "fleshed out" example given in a book written by Larry Wasserman All of statistics on Page 216 (12.8 Strengths and Weaknesses of Bayesian Inference). I basically provide what Wasserman doesn't in his book 1) an explanation for what is actually happening, rather than a throw away line; 2) the frequentist answer to the question, which Wasserman conveniently does not give; and 3) a demonstration that the equivalent confidence calculated using the same information suffers from the same problem.

This is a "fleshed out" example given in a book written by Larry Wasserman All of statistics on Page 216 (12.8 Strengths and Weaknesses of Bayesian Inference). I basically provide what Wasserman doesn't in his book 1) an explanation for what is actually happening, rather than a throw away line; 2) the frequentist answer to the question, which Wasserman conveniently does not give; and 3) a demonstration that the equivalent confidence calculated using the same information suffers from the same problem.

Because we are dealing with a normal distribution with known variance, the mean is a sufficient statistics for constructing a confidence interval for $\theta$. The mean is equal to $\overline{x}=\frac{0+X}{2}=\frac{X}{2}$ and has a sampling distribution

Where $c=\frac{1+\tau^{2}}{1+1+\tau^{2}}$. Thus, plugging in the value at $\tau^{2}=1$ gives $c=\frac{1}{2}$ and the credible interval becomes:

Because we are dealing with a normal distribution with known variance, the mean is a sufficient statistic for constructing a confidence interval for $\theta$. The mean is equal to $\overline{x}=\frac{0+X}{2}=\frac{X}{2}$ and has a sampling distribution

Where $c=\frac{\tau^{2}}{1+\tau^{2}}$. Thus, plugging in the value at $\tau^{2}=1$ gives $c=\frac{1}{2}$ and the credible interval becomes: