**0**wrote:

The limma User's Guide says on p. 62 that the parameter `(s_0)^2`

is the mean of the inverse chi-squared prior for the true residual variances `(sigma_g)^2`

. However, if I get

Smyth, G. K. (2004). Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), 1–25. https://doi.org/10.2202/1544-6115.1027

correctly (more precisely: section 3 (p. 6 bottom)), the prior for the `(sigma_g)^2`

is rather a *scaled* inverse chi-squared distribution with *scaling* parameter `(s_0)^2`

. In that case, the mean is `d_0 * (s_0)^2 / (d_0 - 2)`

(see Wikipedia). So my question is: Am I right that the correct mean for the prior of the `(sigma_g)^2`

is `d_0 * (s_0)^2 / (d_0 - 2)`

and not `(s_0)^2`

?