Borrowing the Irreducible Error

At one fixed test input $x_0$, expected prediction error decomposes as

$\text{Expected MSE} = \text{Bias}^2 + \text{Variance} + \sigma^2_{\text{noise}}$

where $\sigma^2_{\text{noise}}$ is the irreducible noise of the data-generating process at $x_0$. It is a property of the process itself, so it takes the same value for every model fitted to that process. It was never measured directly.

Model A has already been fully profiled at $x_0$:

Model B uses a different learning procedure. Retraining it on independently drawn training sets makes its prediction at $x_0$ a random variable $\hat{Y}$ with the distribution

and the true underlying value at that point is

$f(x_0) = 11$

What is Model B's expected MSE at $x_0$?

Round your answer to 2 decimal places.