{
  "aliases": [
    "uniqueness-theorem-for-characteristic-functions",
    "levy-inversion-formula"
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  "body": "\n## Statement\n\nLet $X$ and $Y$ be real random variables with [[characteristic functions|0MYDNRT7PP]] $\\varphi_X$ and $\\varphi_Y$.\nIf $\\varphi_X(t) = \\varphi_Y(t)$ for all $t \\in \\mathbb{R}$, then $X$ and $Y$ have the same [[law|0TST4MHA3A]].\n\n## Remarks\n\nThe proof uses the Lévy inversion formula. For real $a \u003c b$ that are continuity points of the [[cumulative distribution function|0MXMS3AE13]] $F_X$,\n$$F_X(b) - F_X(a) = \\lim_{T \\to \\infty} \\frac{1}{2\\pi} \\int_{-T}^{T} \\frac{e^{-ita} - e^{-itb}}{it} \\, \\varphi_X(t) \\, dt.$$\nIf $\\int |\\varphi_X(t)| \\, dt \u003c \\infty$, then $X$ has the continuous density $f_X(x) = \\frac{1}{2\\pi} \\int e^{-itx} \\varphi_X(t) \\, dt$.\nThe same result holds for random vectors in $\\mathbb{R}^n$, with $\\varphi_X(t) = E[e^{i \\langle t, X \\rangle}]$.\n\n## Explanations\n\n### L1 human\n\nTwo different distributions cannot have the same characteristic function.\nSo, to identify a distribution, it is enough to compute its characteristic function and recognize it.\n",
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    "name": "The characteristic function determines the distribution",
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        "url": "https://en.wikipedia.org/wiki/Characteristic_function_(probability_theory)"
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  "markdown": "https://math2.tamnd.com/node/0T8BYAFEXX.md",
  "msc": [
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  "name": "The characteristic function determines the distribution",
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  "url": "https://math2.tamnd.com/wiki/the-characteristic-function-determines-the-distribution/",
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