which loss function is used to minimize the expected variance of the posterior mean why is it difficult to minimize the loss of the posterior variance of the experiment? why is the posterior variance converged why is the loss function in decision theory necessary? can you use a loss function in an experiment with a posterior value what is the purpose of the loss function for the decision theory for the posterior mean and the posterior mean of a Bayes experiment? which is optimal for Bayes risk of the posterior model? what is the best way to minimize the bayes risk which type of loss function is optimal for an experiment? why the loss function in bayes risk must be optimized which of the following is a utility function to minimise posterior mean which of the following are losses of a posterior-average? why not minimizing expected variance what is the function of posterior variance in an example why is loss optimal when designing experiments what is the problem with the loss function in planning an experiment what is the purpose of loss function where is expected variance smallest why is posterior mean greater than average when using a loss function which loss functions would minimize the posterior variance of a bayes model?