{"id":9446,"date":"2022-08-17T18:58:40","date_gmt":"2022-08-17T09:58:40","guid":{"rendered":"https:\/\/pandanote.info\/?p=9446"},"modified":"2022-08-17T18:58:40","modified_gmt":"2022-08-17T09:58:40","slug":"%e5%af%be%e6%95%b0%e6%ad%a3%e8%a6%8f%e5%88%86%e5%b8%83%e3%81%8c%e3%83%ad%e3%83%b3%e3%82%b0%e3%83%86%e3%83%bc%e3%83%ab%e3%81%a7%e3%81%82%e3%82%8b%e3%81%93%e3%81%a8%e3%82%92%e7%a4%ba%e3%81%97%e3%81%a6","status":"publish","type":"post","link":"https:\/\/pandanote.info\/?p=9446","title":{"rendered":"\u5bfe\u6570\u6b63\u898f\u5206\u5e03\u304c\u30ed\u30f3\u30b0\u30c6\u30fc\u30eb\u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u307f\u305f\u3002"},"content":{"rendered":"

\u306f\u3058\u3081\u306b<\/h2>\n

\u672cWeb\u30b5\u30a4\u30c8\u306e\u53f3\u5074\u306e\u30b5\u30a4\u30c9\u30d0\u30fc(\u307e\u305f\u306f\u4e0b\u5074)\u306b\u3042\u308b\u691c\u7d22\u7a93\u306b<\/p>\n

\u300c\u5bfe\u6570\u6b63\u898f\u5206\u5e03\u300d<\/p>\n

\u3068\u5165\u529b\u3057\u3066\u691c\u7d22\u3057\u3066\u3082\u306a\u305c\u304b\u3053\u306e\u30da\u30fc\u30b8<\/a>\u304c\u30d2\u30c3\u30c8\u3057\u307e\u305b\u3093\u3067\u3057\u305f\u3002<\/p>\n

\u305d\u3053\u3067\u3001\u3053\u306e\u5927\u5909\u306b\u6b8b\u5ff5\u306a\u72b6\u6cc1\u3092\u306a\u3093\u3068\u304b\u6539\u5584\u3059\u3079\u304f\u3001\u5bfe\u6570\u6b63\u898f\u5206\u5e03\u306e\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570<\/p>\n

\\begin{align}
\np(x) &= \\frac{1}{x\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(\\log x -\\mu)^2}{2\\sigma^2}\\right]\\label{eq:lognormaldistribution}
\n\\end{align}<\/p>\n

\u304c(\u53f3\u5074)\u30ed\u30f3\u30b0\u30c6\u30fc\u30eb\u306a\u5206\u5e03\u3092\u6301\u3064\u3053\u3068\u3092\u793a\u3057\u3066\u307f\u308b\u3053\u3068\u306b\u3057\u307e\u3057\u305f\u3002<\/p>\n

\u30ed\u30f3\u30b0\u30c6\u30fc\u30eb\u306a\u5206\u5e03\u3068\u306f?<\/h2>\n

\u3082\u306e\u306eWikipedia\u306b\u3088\u308a\u307e\u3059\u3068[1<\/a>]\u3001\u78ba\u7387\u5909\u6570$X$\u304c\u3059\u3079\u3066\u306e$t>0$\u306b\u5bfe\u3057\u3066(\\ref{eq:problim})\u5f0f\u3092\u6e80\u305f\u3059\u78ba\u7387\u5206\u5e03\u306e\u3053\u3068\u3092\u30ed\u30f3\u30b0\u30c6\u30fc\u30eb\u3068\u547c\u3076\u306e\u3060\u305d\u3046\u3067\u3059\u3002<\/p>\n

\\begin{align}
\n\\lim_{x\\to\\infty} P(X > x+t|X > x) &= 1\\label{eq:problim}
\n\\end{align}<\/p>\n

(\\ref{eq:problim})\u306b\u304a\u3044\u3066\u3001$P(A|B)$\u306f\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u3092\u8868\u3057\u307e\u3059\u3002<\/p>\n

\u30b5\u30af\u30b5\u30af\u8a08\u7b97\u3002<\/h2>\n

(\\ref{eq:lognormaldistribution})\u5f0f\u304c(\\ref{eq:problim})\u3092\u3059\u3079\u3066\u306e$t>0$\u306b\u5bfe\u3057\u3066\u6e80\u305f\u3059\u3053\u3068\u3092\u8a08\u7b97\u306b\u3088\u3063\u3066\u78ba\u8a8d\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n

\u307e\u305a\u3001\u4ee3\u5165\u3057\u307e\u3059\u3002<\/h3>\n

(\\ref{eq:problim})\u5f0f\u306e\u5de6\u8fba\u306f\u2026<\/p>\n

\\begin{align}
\nP(X > x+t|X > x) &= \\frac{P(X > x+t)}{P(X > x)} \\label{eq:problimsecond}
\n\\end{align}<\/p>\n

\u3068\u5909\u5f62\u3067\u304d\u307e\u3059\u3002<\/p>\n

\u307e\u305f\u3001$P(X > x)$\u306f(\\ref{eq:lognormaldistribution})\u5f0f\u3088\u308a\u3001<\/p>\n

\\begin{align}
\nP(X > x) &= \\int_{x}^{\\infty}p(y)dy \\nonumber \\cr
\n&= \\int_{x}^{\\infty}\\frac{1}{y\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(\\log y -\\mu)^2}{2\\sigma^2}\\right]dy \\label{eq:probfirst}
\n\\end{align}<\/p>\n

\u3068\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

\u540c\u69d8\u306b$P(X > x+t)$\u306b\u3064\u3044\u3066\u3082\u3001<\/p>\n

\\begin{align}
\nP(X > x+t) &= \\int_{x+t}^{\\infty}p(y)dy \\nonumber \\cr
\n&= \\int_{x+t}^{\\infty}\\frac{1}{y\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(\\log y -\\mu)^2}{2\\sigma^2}\\right]dy \\label{eq:probsecond}
\n\\end{align}<\/p>\n

\u3068\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

\u3088\u3063\u3066\u3001(\\ref{eq:problimsecond})\u5f0f\u306f\u2026<\/p>\n

\\begin{align}
\nP(X > x+t|X > x) &= \\dfrac{\\displaystyle\\int_{x+t}^{\\infty}\\frac{1}{y\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(\\log y -\\mu)^2}{2\\sigma^2}\\right]dy}{\\displaystyle\\int_{x}^{\\infty}\\frac{1}{y\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(\\log y -\\mu)^2}{2\\sigma^2}\\right]dy} \\label{eq:problimthird}
\n\\end{align}<\/p>\n

\u3068\u306a\u308a\u307e\u3059\u3002\u3053\u3053\u307e\u3067\u306e\u5f0f\u3067\u7a4d\u5206\u306e\u5909\u6570\u304c$y$\u306b\u306a\u3063\u3066\u3044\u308b\u306e\u306b\u306f\u7279\u306b\u610f\u5473\u306f\u3042\u308a\u307e\u305b\u3093\u306e\u3067\u3001\u6c17\u306b\u3057\u306a\u3044\u65b9\u5411\u3067\u304a\u9858\u3044\u3044\u305f\u3057\u307e\u3059\u3002<\/p>\n

\u3055\u3089\u306b\u3001$u = \\log y$\u3068\u304a\u3044\u3066\u3001(\\ref{eq:problimthird})\u5f0f\u306e\u5206\u5b50\u3068\u5206\u6bcd\u306e\u7a4d\u5206\u5f0f\u306e\u5909\u6570\u5909\u63db\u3092\u884c\u3046\u3068\u2026<\/p>\n

\\begin{align}
\nP(X > x+t|X > x) &= \\dfrac{\\displaystyle\\int_{\\log(x+t)}^{\\infty}\\frac{1}{e^u\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(u -\\mu)^2}{2\\sigma^2}\\right]\\dfrac{dy}{du}du}{\\displaystyle\\int_{\\log(x)}^{\\infty}\\frac{1}{e^u\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(u -\\mu)^2}{2\\sigma^2}\\right]\\dfrac{dy}{du}du} \\nonumber \\cr
\n&= \\dfrac{\\displaystyle\\int_{\\log(x+t)}^{\\infty}\\frac{1}{e^u\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(u -\\mu)^2}{2\\sigma^2}\\right]e^udu}{\\displaystyle\\int_{\\log(x)}^{\\infty}\\frac{1}{e^u\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(u -\\mu)^2}{2\\sigma^2}\\right]e^udu} \\nonumber \\cr
\n&= \\dfrac{\\displaystyle\\int_{\\log(x+t)}^{\\infty}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(u -\\mu)^2}{2\\sigma^2}\\right]du}{\\displaystyle\\int_{\\log(x)}^{\\infty}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(u -\\mu)^2}{2\\sigma^2}\\right]du}\\label{eq:problimfourth}
\n\\end{align}<\/p>\n

\u3068\u5909\u5f62\u3067\u304d\u3066\u3001\u5206\u6bcd\u53ca\u3073\u5206\u5b50\u306b\u6b63\u898f\u5206\u5e03\u306e\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570\u3092\u7a4d\u5206\u3057\u305f\u3082\u306e\u304c\u73fe\u308c\u307e\u3059\u3002<\/p>\n

\u30ed\u30d4\u30bf\u30eb\u306e\u5b9a\u7406\u3092\u4f7f\u3046\u3002<\/h3>\n

(\\ref{eq:problimfourth})\u5f0f\u306f\u76f4\u63a5\u8a08\u7b97\u3059\u308b\u3053\u3068\u304c\u96e3\u3057\u305d\u3046\u3067\u3059\u3002<\/p>\n

\u30c6\u30a4\u30e9\u30fc\u5c55\u958b\u3092\u3057\u3066\u3082\u8a08\u7b97\u304c\u6357\u308b\u3053\u3068\u306f\u306a\u3055\u305d\u3046\u3067\u3059\u3002<\/p>\n

\u305d\u3053\u3067\u3001\u30ed\u30d4\u30bf\u30eb\u306e\u5b9a\u7406\u304c\u9069\u7528\u3067\u304d\u308b\u304b\u3069\u3046\u304b\u3092\u691c\u8a0e\u3057\u3001\u5206\u5b50\u53ca\u3073\u5206\u6bcd\u306e\u5f0f\u3092\u3082\u3046\u5c11\u3057\u7c21\u5358\u306b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u306a\u3044\u304b\u8003\u3048\u307e\u3059\u3002<\/p>\n

$x \\to \\infty$\u306e\u6642\u306b\u5206\u6bcd\u53ca\u3073\u5206\u5b50\u304c\u3068\u3082\u306b0\u306b\u306a\u308b\u3053\u3068\u3068\u3001(\\ref{eq:problimfourth})\u5f0f\u306e\u5206\u6bcd\u3092$x$\u3067\u5fae\u5206\u3057\u305f\u5f0f<\/p>\n

\\begin{align}
\n\\dfrac{d}{dx}\\displaystyle\\int_{\\log(x)}^{\\infty}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(u -\\mu)^2}{2\\sigma^2}\\right]du &= -\\frac{1}{x\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(\\log x -\\mu)^2}{2\\sigma^2}\\right] \\label{eq:denominator}
\n\\end{align}<\/p>\n

\u304c$x > 0$\u3067\u306f0\u306b\u306a\u3089\u306a\u3044\u3053\u3068\u3092\u78ba\u8a8d\u306e\u4e0a\u3001(\\ref{eq:problimfourth})\u5f0f\u306e\u5206\u5b50\u53ca\u3073\u5206\u6bcd\u3092\u305d\u308c\u305e\u308c$x$\u3067\u5fae\u5206\u3057\u3001\u305d\u306e\u7d50\u679c\u5f97\u3089\u308c\u305f\u5f0f\u306e$x \\to \\infty$\u306b\u304a\u3051\u308b\u6975\u9650\u3092\u8a08\u7b97\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n

\u4e0a\u8a18\u306e\u6975\u9650\u304c\u5b58\u5728\u3059\u308c\u3070\u3001\u30ed\u30d4\u30bf\u30eb\u306e\u5b9a\u7406\u3092\u9069\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

(\\ref{eq:problimfourth})\u5f0f\u306e\u5206\u5b50\u53ca\u3073\u5206\u6bcd\u3092$x$\u3067\u5fae\u5206\u3057\u305f\u5f0f\u306f\u2026<\/p>\n

\\begin{align}
\n\\dfrac{\\dfrac{d}{dx}\\displaystyle\\int_{\\log(x+t)}^{\\infty}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(u -\\mu)^2}{2\\sigma^2}\\right]du}{\\dfrac{d}{dx}\\displaystyle\\int_{\\log(x)}^{\\infty}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(u -\\mu)^2}{2\\sigma^2}\\right]du} &= \\dfrac{-\\dfrac{1}{(x+t)\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(\\log (x+t) -\\mu)^2}{2\\sigma^2}\\right]}{-\\dfrac{1}{x\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(\\log x -\\mu)^2}{2\\sigma^2}\\right]} \\nonumber \\cr
\n&= \\dfrac{x}{x+t}\\,\\exp\\left[-\\dfrac{(\\log (x+t) -\\mu)^2-(\\log x -\\mu)^2}{2\\sigma^2}\\right] \\nonumber \\cr
\n&= \\dfrac{x}{x+t}\\,\\exp\\left[-\\dfrac{(\\log (x+t) -\\mu)^2}{2\\sigma^2}\\left[1-\\dfrac{(\\log x -\\mu)^2}{(\\log (x+t) -\\mu)^2}\\right]\\right] \\nonumber \\cr
\n&= \\dfrac{x}{x+t}\\left[\\exp\\left[-\\dfrac{(\\log (x+t) -\\mu)^2}{2\\sigma^2}\\right]\\right]^\\left[1-\\frac{(\\log x -\\mu)^2}{(\\log (x+t) -\\mu)^2}\\right] \\label{eq:problimfifth}
\n\\end{align}<\/p>\n

\u306e\u3088\u3046\u306b\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/p>\n

\u3055\u3089\u306b\u3001(\\ref{eq:problimfifth})\u5f0f\u306e\u53f3\u8fba\u3092$x \\to \\infty$\u3068\u3057\u305f\u3068\u304d\u306e\u6975\u9650\u3092\u3068\u308a\u307e\u3059\u3002<\/p>\n

\u3053\u3053\u3067\u3001<\/p>\n

\\begin{align}
\n\\lim_{x \\to \\infty} \\dfrac{x}{x+t} &= 1 \\label{eq:limxxt} \\cr
\n\\lim_{x \\to \\infty} \\exp\\left[-\\dfrac{(\\log (x+t) -\\mu)^2}{2\\sigma^2}\\right] &= 0 \\label{eq:limexp}
\n\\end{align}<\/p>\n

\u3068\u306a\u308b\u3053\u3068\u3068\u3001<\/p>\n

\\begin{align}
\n\\lim_{x \\to \\infty} \\frac{(\\log x -\\mu)^2}{(\\log(x+t) -\\mu)^2} &= \\lim_{x \\to \\infty} \\dfrac{\\dfrac{2}{x}(\\log x – \\mu)}{\\dfrac{2}{x+t}(\\log(x+t) – \\mu)} \\label{eq:lhopitalfirst} \\cr
\n&= \\lim_{x \\to \\infty} \\dfrac{(x+t)(\\log x – \\mu)}{x(\\log(x+t) – \\mu)} \\nonumber \\cr
\n&= \\left[ \\lim_{x \\to \\infty} \\dfrac{x+t}{x} \\right] \\left[\\lim_{x \\to \\infty} \\dfrac{\\log x – \\mu}{\\log(x+t) – \\mu} \\right] \\nonumber \\cr
\n&= \\lim_{x \\to \\infty} \\dfrac{x}{x+t} \\label{eq:lhopitalsecond} \\cr
\n&= 1 \\label{eq:limlogfirst}
\n\\end{align}<\/p>\n

\u3067\u3042\u308b($x > e^{\\mu}-t$\u3067\u3042\u308c\u3070(\\ref{eq:lhopitalfirst})\u5f0f\u53f3\u8fba\u306e\u5206\u6bcd\u304c0\u306b\u306f\u306a\u3089\u306a\u3044\u3053\u3068\u3001\u307e\u305f$x > 0$\u3067\u3042\u308c\u3070(\\ref{eq:lhopitalsecond})\u5f0f\u53f3\u8fba\u306e\u5206\u6bcd\u306f0\u306b\u306f\u306a\u3089\u306a\u3044\u3053\u3068\u304b\u3089(\\ref{eq:lhopitalfirst})\u5f0f\u53ca\u3073(\\ref{eq:lhopitalsecond})\u5f0f\u306e\u4e21\u5f0f\u306b\u5bfe\u3057\u3066\u30ed\u30d4\u30bf\u30eb\u306e\u5b9a\u7406\u3092\u4f7f\u7528\u3002)\u3053\u3068\u304b\u3089\u3001<\/p>\n

\\begin{align}
\n\\lim_{x \\to \\infty} \\left[ 1 – \\frac{(\\log x -\\mu)^2}{(\\log(x+t) -\\mu)^2} \\right] &= 0 \\label{eq:limlogfinal}
\n\\end{align}<\/p>\n

\u3068\u8a08\u7b97\u3067\u304d\u308b\u3053\u3068\u3092\u5229\u7528\u3057\u307e\u3059\u3002<\/p>\n

(\\ref{eq:limxxt})\u3001(\\ref{eq:limexp})\u53ca\u3073(\\ref{eq:limlogfinal})\u306e\u5404\u5f0f\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u3001(\\ref{eq:problimfifth})\u5f0f\u306e\u53f3\u8fba\u3092$x \\to \\infty$\u3068\u3057\u305f\u3068\u304d\u306e\u6975\u9650\u306f\u3001<\/p>\n

\\begin{align}
\n\\lim_{x \\to \\infty} \\dfrac{x}{x+t}\\left[\\exp\\left[-\\dfrac{(\\log (x+t) -\\mu)^2}{2\\sigma^2}\\right]\\right]^\\left[1-\\frac{(\\log (x+t) -\\mu)^2}{(\\log x -\\mu)^2}\\right] &= 1\\cdot 0^0 \\nonumber \\cr
\n&= 1 \\label{eq:problimsixth}
\n\\end{align}<\/p>\n

\u3068\u8a08\u7b97\u3067\u304d\u307e\u3059($0^0 = 1$\u3068\u3057\u3066\u3044\u307e\u3059)\u3002<\/p>\n

(\\ref{eq:problimfourth})\u306e\u5de6\u8fba\u304c1\u3067\u3042\u308b\u3053\u3068\u3001\u3059\u306a\u308f\u3061\u6975\u9650\u304c\u5b58\u5728\u3059\u308b\u3053\u3068\u304c\u308f\u304b\u3063\u305f\u306e\u3067\u3001(\\ref{eq:problimsecond})\u5f0f\u304c\u6210\u7acb\u3059\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002$\\blacksquare$<\/p>\n

\u307e\u3068\u3081<\/h2>\n

\u9069\u7528\u6761\u4ef6\u3092\u614e\u91cd\u306b\u78ba\u8a8d\u3057\u3064\u3064\u3001\u666e\u6bb5\u306f\u3042\u307e\u308a\u4f7f\u308f\u306a\u3044\u30ed\u30d4\u30bf\u30eb\u306e\u5b9a\u7406\u3092\u4f7f\u3063\u3066\u8a08\u7b97\u3057\u3066\u307f\u305f\u308a\u3001$0^0 = 1$\u3068\u3057\u3066\u307f\u305f\u308a\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u5bfe\u6570\u6b63\u898f\u5206\u5e03\u304c\u30ed\u30f3\u30b0\u30c6\u30fc\u30eb\u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n

\u4f55\u304b\u306e\u6a5f\u4f1a\u306b\u53c2\u8003\u306b\u3057\u3066\u3044\u305f\u3060\u3051\u308c\u3070\u5e78\u3044\u3067\u3059\u3002<\/p>\n

\u3053\u306e\u8a18\u4e8b\u306f\u4ee5\u4e0a\u3067\u3059\u3002<\/p>\n

References \/ \u53c2\u8003\u6587\u732e<\/h2>\n
    \n
  1. \u88fe\u306e\u91cd\u3044\u5206\u5e03<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    \u306f\u3058\u3081\u306b \u672cWeb\u30b5\u30a4\u30c8\u306e\u53f3\u5074\u306e\u30b5\u30a4\u30c9\u30d0\u30fc(\u307e\u305f\u306f\u4e0b\u5074)\u306b\u3042\u308b\u691c\u7d22\u7a93\u306b \u300c\u5bfe\u6570\u6b63\u898f\u5206\u5e03\u300d \u3068\u5165\u529b\u3057\u3066\u691c\u7d22\u3057\u3066\u3082\u306a\u305c\u304b\u3053\u306e\u30da\u30fc\u30b8\u304c\u30d2\u30c3\u30c8\u3057\u307e\u305b\u3093\u3067\u3057\u305f\u3002 \u305d\u3053\u3067\u3001\u3053\u306e\u5927\u5909\u306b\u6b8b\u5ff5\u306a\u72b6\u6cc1\u3092\u306a\u3093\u3068\u304b\u6539\u5584\u3059\u3079\u304f\u3001\u5bfe\u6570\u6b63\u898f\u5206\u5e03\u306e\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570 \\begin{align} p(x) &= \\frac{1}{x\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(\\log x -\\m\u2026 Read More »<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":8688,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"class_list":["post-9446","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-13"],"_links":{"self":[{"href":"https:\/\/pandanote.info\/index.php?rest_route=\/wp\/v2\/posts\/9446","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pandanote.info\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pandanote.info\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pandanote.info\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/pandanote.info\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=9446"}],"version-history":[{"count":25,"href":"https:\/\/pandanote.info\/index.php?rest_route=\/wp\/v2\/posts\/9446\/revisions"}],"predecessor-version":[{"id":9472,"href":"https:\/\/pandanote.info\/index.php?rest_route=\/wp\/v2\/posts\/9446\/revisions\/9472"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pandanote.info\/index.php?rest_route=\/wp\/v2\/media\/8688"}],"wp:attachment":[{"href":"https:\/\/pandanote.info\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9446"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pandanote.info\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9446"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pandanote.info\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9446"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}