Let $\Lambda \sim \mathcal W_D(\nu, \Psi)$, i.e. distributed according to a $D \times D$ dimensional Wishart distribution with mean $\nu \Psi$ and degrees of freedom $\nu$. I would like an expression for $E(\log |\Lambda|)$ where $|\Lambda|$ is the determinant.
I've google'd a bit for the answer to this and have gotten some conflicting information. This paper explicitly states that $$ E(\log|\Lambda|) = D \log 2 + \log |\Psi| + \sum_{i = 1} ^ D \psi\left(\frac{\nu - i + 1} 2\right) $$ where $\psi(\cdot)$ denotes the digamma function $\frac d {dx} \log \Gamma(x)$; the paper does not give a source for this fact as far as I can tell. This is also the formula used on the wikipedia page for the Wishart, which sites Bishop's Pattern Recognition text.
On the other hand, google turned up this discussion with a linked paper that states that $$ \nu^D \frac{|\Lambda|}{|\Psi|} \sim \chi^2_\nu \chi^2_{\nu - 1} \cdots\chi^2_{\nu - D + 1}. \qquad (\dagger) $$ They conclude by stating that $$ E(\log | \Lambda|) = D \log 2 - D \log \nu + \log |\Psi| + \sum_{i = 1} ^ D \psi\left(\frac{\nu - i + 1} 2\right) $$ which is derived using the fact that $E(\log \chi^2_\nu) = \log(2) + \psi(\nu /2)$. I checked this calculation starting from $(\dagger)$ and it seems okay, but we have an extra $-D\log\nu$.