Consider data Y1,Y2,…Yn that is a random (i.e. independent) sample from a Normal distribution with unknown mean μ and known variance σ2. You wish to infer μ from the data using a Bayesian approach and select a prior on μthat is Normal with mean μ0 and variance τ2. a. Derive the posterior distribution for μ. Since you should know the final result, credit will only be given for the derivation. b. Write down the posterior mean as a function of the posterior variance. Explain what happens to the posterior mean as a function of increasing n. c. Confirm your answer from part b) by plotting your results in R assuming μ0=0,τ2=1,σ2=100, and with the data Yˉ=5 for n=1,10,100,1000.
Consider data Y1,Y2,…Yn that is a random (i.e. independent) sample from a
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