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Steffo 2020-01-28 02:33:09 +01:00
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commit 05515afc68
12 changed files with 12 additions and 11 deletions
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.gitattributes vendored Normal file
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build/* linguist-generated
docs/* linguist-generated

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uni.steffo.eu

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{"/":{"style.5336e.css":{"type":"style","weight":1},"bundle.7a595.js":{"type":"script","weight":1}}} {"/":{"style.5336e.css":{"type":"style","weight":1},"bundle.24f07.js":{"type":"script","weight":1}}}

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docs/ssr-build/ssr-bundle.js
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@ -10802,7 +10802,7 @@ var statistica__templateObject = statistica__taggedTemplateLiteralLoose(['P(E) =
statistica__templateObject71 = statistica__taggedTemplateLiteralLoose(['E(X) = overline{k} cdot P(X < k) + k cdot P(X geq k)'], ['E(X) = \\overline{k} \\cdot P(X < k) + k \\cdot P(X \\geq k)']), statistica__templateObject71 = statistica__taggedTemplateLiteralLoose(['E(X) = overline{k} cdot P(X < k) + k cdot P(X geq k)'], ['E(X) = \\overline{k} \\cdot P(X < k) + k \\cdot P(X \\geq k)']),
statistica__templateObject72 = statistica__taggedTemplateLiteralLoose(['epsilon'], ['\\epsilon']), statistica__templateObject72 = statistica__taggedTemplateLiteralLoose(['epsilon'], ['\\epsilon']),
statistica__templateObject73 = statistica__taggedTemplateLiteralLoose(['\frac{Var(X)}{epsilon^2}'], ['\\frac{Var(X)}{\\epsilon^2}']), statistica__templateObject73 = statistica__taggedTemplateLiteralLoose(['\frac{Var(X)}{epsilon^2}'], ['\\frac{Var(X)}{\\epsilon^2}']),
statistica__templateObject74 = statistica__taggedTemplateLiteralLoose(['\forall epsilon > 0, P([ -epsilon leq left( X - E(X) \right) leq epsilon]) leq \frac{Var(X)}{epsilon^2}'], ['\\forall \\epsilon > 0, P([ -\\epsilon \\leq \\left( X - E(X) \\right) \\leq \\epsilon]) \\leq \\frac{Var(X)}{\\epsilon^2}']), statistica__templateObject74 = statistica__taggedTemplateLiteralLoose(['\forall epsilon > 0, P([ left| X - E(X) \right| geq epsilon]) leq \frac{Var(X)}{epsilon^2}'], ['\\forall \\epsilon > 0, P([ \\left| X - E(X) \\right| \\geq \\epsilon]) \\leq \\frac{Var(X)}{\\epsilon^2}']),
statistica__templateObject75 = statistica__taggedTemplateLiteralLoose(['mu_k = E ( X^k ) = \begin{cases}\n sum_i x_i^k p_X (x_i) qquad nel discreto\\\n \\\n int_{-infty}^{+infty} x^k f_X (x) dx qquad nel continuo\n end{cases}'], ['\\mu_k = E ( X^k ) = \\begin{cases}\n \\sum_i x_i^k p_X (x_i) \\qquad nel\\ discreto\\\\\n \\\\\n \\int_{-\\infty}^{+\\infty} x^k f_X (x) dx \\qquad nel\\ continuo\n \\end{cases}']), statistica__templateObject75 = statistica__taggedTemplateLiteralLoose(['mu_k = E ( X^k ) = \begin{cases}\n sum_i x_i^k p_X (x_i) qquad nel discreto\\\n \\\n int_{-infty}^{+infty} x^k f_X (x) dx qquad nel continuo\n end{cases}'], ['\\mu_k = E ( X^k ) = \\begin{cases}\n \\sum_i x_i^k p_X (x_i) \\qquad nel\\ discreto\\\\\n \\\\\n \\int_{-\\infty}^{+\\infty} x^k f_X (x) dx \\qquad nel\\ continuo\n \\end{cases}']),
statistica__templateObject76 = statistica__taggedTemplateLiteralLoose(['m_X (t) = E( e^{t cdot X} )'], ['m_X (t) = E( e^{t \\cdot X} )']), statistica__templateObject76 = statistica__taggedTemplateLiteralLoose(['m_X (t) = E( e^{t cdot X} )'], ['m_X (t) = E( e^{t \\cdot X} )']),
statistica__templateObject77 = statistica__taggedTemplateLiteralLoose(['H_X (t) = E ( e^{i cdot t cdot X} )'], ['H_X (t) = E ( e^{i \\cdot t \\cdot X} )']), statistica__templateObject77 = statistica__taggedTemplateLiteralLoose(['H_X (t) = E ( e^{i cdot t cdot X} )'], ['H_X (t) = E ( e^{i \\cdot t \\cdot X} )']),

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"use strict";var precacheConfig=[["/assets/favicon.ico","468bad0a3f47a965985c8a99f3ab0ced"],["/assets/icon.png","2b714cf154cd80b3949545dfe0c5939a"],["/bundle.24f07.js","b2efc9195f7a80c48698957e752bf2dd"],["/favicon.ico","468bad0a3f47a965985c8a99f3ab0ced"],["/index.html","4916b54530a5995074530394d18d3ad6"],["/manifest.json","451674ae8f01fd50402a3afd4ebeeffa"],["/style.5336e.css","bf9341495a45d7236460e2af436a3b33"]],cacheName="sw-precache-v3-sw-precache-webpack-plugin-"+(self.registration?self.registration.scope:""),ignoreUrlParametersMatching=[/^utm_/],addDirectoryIndex=function(e,t){var n=new URL(e);return"/"===n.pathname.slice(-1)&&(n.pathname+=t),n.toString()},cleanResponse=function(e){return e.redirected?("body"in e?Promise.resolve(e.body):e.blob()).then(function(t){return new Response(t,{headers:e.headers,status:e.status,statusText:e.statusText})}):Promise.resolve(e)},createCacheKey=function(e,t,n,r){var a=new URL(e);return r&&a.pathname.match(r)||(a.search+=(a.search?"&":"")+encodeURIComponent(t)+"="+encodeURIComponent(n)),a.toString()},isPathWhitelisted=function(e,t){if(0===e.length)return!0;var n=new URL(t).pathname;return e.some(function(e){return n.match(e)})},stripIgnoredUrlParameters=function(e,t){var n=new URL(e);return n.hash="",n.search=n.search.slice(1).split("&").map(function(e){return e.split("=")}).filter(function(e){return t.every(function(t){return!t.test(e[0])})}).map(function(e){return e.join("=")}).join("&"),n.toString()},hashParamName="_sw-precache",urlsToCacheKeys=new Map(precacheConfig.map(function(e){var t=e[0],n=e[1],r=new URL(t,self.location),a=createCacheKey(r,hashParamName,n,!1);return[r.toString(),a]}));function setOfCachedUrls(e){return e.keys().then(function(e){return e.map(function(e){return e.url})}).then(function(e){return new Set(e)})}self.addEventListener("install",function(e){e.waitUntil(caches.open(cacheName).then(function(e){return setOfCachedUrls(e).then(function(t){return Promise.all(Array.from(urlsToCacheKeys.values()).map(function(n){if(!t.has(n)){var r=new Request(n,{credentials:"same-origin"});return fetch(r).then(function(t){if(!t.ok)throw new Error("Request for "+n+" returned a response with status "+t.status);return cleanResponse(t).then(function(t){return e.put(n,t)})})}}))})}).then(function(){return self.skipWaiting()}))}),self.addEventListener("activate",function(e){var t=new Set(urlsToCacheKeys.values());e.waitUntil(caches.open(cacheName).then(function(e){return e.keys().then(function(n){return Promise.all(n.map(function(n){if(!t.has(n.url))return e.delete(n)}))})}).then(function(){return self.clients.claim()}))}),self.addEventListener("fetch",function(e){if("GET"===e.request.method){var t,n=stripIgnoredUrlParameters(e.request.url,ignoreUrlParametersMatching);(t=urlsToCacheKeys.has(n))||(n=addDirectoryIndex(n,"index.html"),t=urlsToCacheKeys.has(n));!t&&"navigate"===e.request.mode&&isPathWhitelisted(["^(?!\\/__).*"],e.request.url)&&(n=new URL("index.html",self.location).toString(),t=urlsToCacheKeys.has(n)),t&&e.respondWith(caches.open(cacheName).then(function(e){return e.match(urlsToCacheKeys.get(n)).then(function(e){if(e)return e;throw Error("The cached response that was expected is missing.")})}).catch(function(t){return console.warn('Couldn\'t serve response for "%s" from cache: %O',e.request.url,t),fetch(e.request)}))}});

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src/pages/statistica.js
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@ -726,7 +726,7 @@ export default class Statistica extends Component {
Se la variabile aleatoria <Latex>X</Latex> ha media e varianza, allora la probabilità che essa abbia un valore a più di <Latex>{r`\epsilon`}</Latex> di distanza dal valore medio è minore o uguale a <Latex>{r`\frac{Var(X)}{\epsilon^2}`}</Latex>. Se la variabile aleatoria <Latex>X</Latex> ha media e varianza, allora la probabilità che essa abbia un valore a più di <Latex>{r`\epsilon`}</Latex> di distanza dal valore medio è minore o uguale a <Latex>{r`\frac{Var(X)}{\epsilon^2}`}</Latex>.
</p> </p>
<p> <p>
<Latex>{r`\forall \epsilon > 0, P([ -\epsilon \leq \left( X - E(X) \right) \leq \epsilon]) \leq \frac{Var(X)}{\epsilon^2}`}</Latex> <Latex>{r`\forall \epsilon > 0, P([ \left| X - E(X) \right| \geq \epsilon]) \leq \frac{Var(X)}{\epsilon^2}`}</Latex>
</p> </p>
<Example> <Example>
Serve per semplificare i calcoli quando la funzione di ripartizione è difficile da calcolare! Serve per semplificare i calcoli quando la funzione di ripartizione è difficile da calcolare!