When data were antilogarithm-transformed, giving rise to lognormal distributions, samroc again came out best, followed by the Bayes method.

The Bayes method seems to achieve best when the number of false positives is allowed to grow rather large.

Probably SAM or the Bayes method is more useful in these situations.

In the following, we work through this model in great detail to illustrate the workings of the variational Bayes method.

Robbins introduced this proposition while developing empirical Bayes methods.

The Robbins lemma, used in empirical Bayes methods, is named after him.

Bayes methods are more cost effective than the traditional frequentist take on marketing research and subsequent decision making.

Here, the notation means the smoothed or adjusted value of the frequency shown in parenthesis (see also empirical Bayes method).

Empirical Bayes methods are procedures for statistical inference in which the prior distribution is estimated from the data.

Robust Bayes methods acknowledge that it is sometimes very difficult to come up with precise distributions to be used as priors.