What is the difference between the risk function used in Bayesian inference and the one used in supervised learning? In the context of Bayesian inference, given

*

*the random parameter $\Theta$,

*the observed data $\mathcal{D} = \{x_1,x_2,\dots,x_N\}$,

*the posterior $p(\theta\mid \mathcal{D})$,

*the estimator $\hat\theta(\mathcal{D})$,

*and the loss function $L\left(\hat\theta(\mathcal{D}),\Theta\right)$,

the Bayesian risk function is defined as
$$
R_B\left(\hat\theta,\mathcal{D}\right) = \mathbb{E}_{p(\theta\mid\mathcal{D})}\left[L\left(\hat\theta(\mathcal{D}),\Theta\right) \right] = \int_\theta L\left(\hat\theta(\mathcal{D}),\theta\right) \cdot p(\theta\mid\mathcal{D}) \ \text{d}\theta
$$
In contrast, in the context of supervised learning, given

*

*the joint distribution $p(x,y)$,

*the hypothesis $h(x)$,

*and the loss function $L\left(h(x),y\right)$,

the supervised learning risk function is defined as
$$
R_{SL}\left(h\right) = \mathbb{E}_{p(x,y)}\left[L\left(h(X),Y\right) \right] = \int_x \int_y L\left(h(x),y\right) \cdot p(x,y) \ \text{d}x \ \text{d}y
$$
Is there a relationship between $R_B\left(\hat\theta,\mathcal{D}\right)$ and $R_{SL}\left(h\right)$?

If we let
$$
Y := \Theta \\
h := \hat\theta
$$
then $R_{SL}(h)$ becomes
$$
R_{SL}(\hat\theta) = \mathbb{E}_{p(x,\theta)}\left[L\left(\hat\theta(X),\Theta\right) \right]
$$
Using the law of total expectation,
\begin{align}
R_{SL}(\hat\theta) &= \mathbb{E}_{p(x,\theta)}\left[L\left(\hat\theta(X),\Theta\right) \right]\\
&= \mathbb{E}_{p(x)}\left[\mathbb{E}_{p(\theta\mid x)}\left[L\left(\hat\theta(X),\Theta\right) \right] \right]
\end{align}
Without loss of generality, if we let
$$
X := (X_1,X_2,\dots,X_N)
$$
then
\begin{align}
R_{SL}(\hat\theta) &= \mathbb{E}_{p(x)}\left[\mathbb{E}_{p(\theta\mid x)}\left[L\left(\hat\theta(X),\Theta\right) \right] \right] \\
&= \mathbb{E}_{p(\mathcal{D})}\left[\mathbb{E}_{p(\theta\mid \mathcal{D})}\left[L\left(\hat\theta(\mathcal{D}),\Theta\right) \right] \right]
\end{align}
Note that
$$
\mathbb{E}_{p(\theta\mid \mathcal{D})}\left[L\left(\hat\theta(\mathcal{D}),\Theta\right) \right] = R_B(\hat\theta,\mathcal{D})
$$
and so
$$
R_{SL}(\hat\theta) = \mathbb{E}_{p(\mathcal{D})}\left[R_B(\hat\theta,\mathcal{D})\right]
$$
This means that $R_{SL}(\hat\theta)$ is just the Bayesian risk averaged over all possible datasets $\mathcal{D}$.
