Consider Bayes' formula as discussed from the lecture. Which of the following pieces of information are definitely necessary in order to use Bayes' formula to compute the posterior? (Choose all that apply.)

Question

The mean of the n observations

The Fisher information of the prior distribution \pi (\lambda )

The likelihood function L(X_1, X_2, \ldots , X_ n | \lambda ) of the observations

The value of the prior distribution \pi (\lambda ) at every point where it is defined.

Answer 1

The likelihood function L(X_1, X_2, \ldots , X_ n | \lambda ) of the observations

The value of the prior distribution \pi (\lambda ) at every point where it is defined.