Quadratic posterior deviance moments ($p_D=\mathrm{tr}(J\Sigma )$)

Suppose the Bayesian deviance is locally $D(\theta )=D(\hat{\theta })+(\theta -\hat{\theta }{)}^{T}J(\theta -\hat{\theta })$ with positive-definite $J$, and the posterior is normal with mean $m$ and covariance $\Sigma$. Writing $b=m-\hat{\theta }$, expectation and variance of the quadratic form giveConsequently the usual effective parameter count in DIC is $p_D=\mathrm{tr}(J\Sigma )$, whereas half the deviance variance is $\mathrm{tr}\{(J\Sigma {)}^2\}+2b^{T}J\Sigma Jb$. A locally flat prior gives $m=\hat{\theta }$ and $\Sigma =J^{-1}$, so both counts equal the parameter dimension. Informative priors generally destroy their equality. The expectation formula requires only the first two moments; normality is used for the variance formula.