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  "body": "\n## Definition\n\nLet $\\set{P_\\theta : \\theta \\in \\Theta}$ be a [[statistical model|K3AMMSY8DD]] for an observation $X$ with values in a measurable space $\\mathcal{X}$.\nLet $\\mathcal{A}$ be a measurable space of actions and let $L \\colon \\Theta \\times \\mathcal{A} \\to [0, \\infty)$ be a [[loss function|3BTG6VVCGZ]], measurable in $a$ for each $\\theta$.\nA (non-randomized) *decision rule* is a measurable map $\\delta \\colon \\mathcal{X} \\to \\mathcal{A}$.\nThe *risk function* of $\\delta$ is the map $R(\\cdot, \\delta) \\colon \\Theta \\to [0, \\infty]$ given by\n$$R(\\theta, \\delta) = E_\\theta\\big[ L(\\theta, \\delta(X)) \\big] = \\int_{\\mathcal{X}} L(\\theta, \\delta(x)) \\, P_\\theta(dx).$$\n\n## Remarks\n\nThe risk is an [[expected value|0WQNRCNY3M]] over the data, for a fixed $\\theta$.\nUnder the squared error loss $L(\\theta, a) = (a - g(\\theta))^2$, the risk of an estimator is its [[mean squared error|05NZ1HHQMV]].\nFor randomized rules, one also averages over the randomization.\n\nExample: let $X_1, \\dots, X_n$ be independent [[normal|05AQE0QR0D]] with mean $\\theta$ and variance $1$, with squared error loss.\nFor $\\delta = \\bar X$, the risk is $R(\\theta, \\delta) = E_\\theta[(\\bar X - \\theta)^2] = \\operatorname{Var}_\\theta(\\bar X) = 1/n$ for every $\\theta$.\n\nNon-example: the realized loss $L(\\theta, \\delta(x))$ at the observed data $x$ is not the risk.\nIt depends on the data, while the risk is an average over all possible data.\n",
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