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Given a true normal distribution $g(x)$ with mean $\mu_G$ and variance $\sigma_G$, and a model $f(x)$, the KL divergence involves the expected log-likelihood $\mathbb{E}_G[log f(x|\theta]$. The likelihood function is given by $$log f(x|\mathbb{\theta})=-\frac{1}{2}log(2\pi\sigma^2)-\frac{(x-\mu)^2}{2\sigma^2}$$

I read, that $\mathbb{E}_G[log f(x)|\theta]$ equals $$-\frac{1}{2}log(2\pi\sigma^2)-\sigma_G^2+\frac{(\mu-\mu_G)^2}{\sigma^2}$$

but I do not understand how this result($\sigma_G^2+\frac{(\mu-\mu_G)^2}{\sigma^2}$) is obtained.

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Hint: \begin{align} \int (x - \mu)^2 g(x) dx &= \int (x -\mu_G + \mu_G - \mu)^2 g(x) dx \\ &= \int(x - \mu_G)^2 g(x) dx + \int (\mu_G - \mu)^2 g(x) dx + 2\int (x - \mu_G)(\mu_G - \mu)g(x) dx \\ &=\sigma_G^2 + (\mu_G - \mu)^2 + 0 \end{align}

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