I'm trying to understand, why the MSE of two Random Variables $\Theta$ and X is: $ E[] $ is the expectation and $f_X(x)$ is the probability density function of X
$$ E[(T(X)-\Theta)^2] = E[E[(T(X)-\Theta)^2|\Theta]] = \int_\Theta \int_X (T(x)-\Theta)^2f_{x,\Theta}(x,\theta)dxd\theta$$
$$E[(T(X)-\Theta)^2] = E[T(X)^2-2T(X)\Theta + \Theta^2] \underbrace{=}_{linear} E[T(X)^2] - 2E[T(X)\Theta]+E[\Theta^2] = \int_X T(x)^2 f_X(x)dx - 2\int_X\int_\Theta T(x)\theta f_{x,\theta} d\theta dx +\int_\Theta \theta^2 f_\Theta(\theta) d\theta$$
But now I don't know how do I get the equation from above.