. . "A-priori-Verteilung"@de . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "\u5148\u9A8C\u6982\u7387"@zh . . . . "27834"^^ . . . . "\uC0AC\uC804 \uD655\uB960(\u4E8B\u524D\u78BA\u7387, \uC601\uC5B4: prior probability)\uC740 \uD2B9\uC815 \uC0AC\uC0C1\uC774 \uC77C\uC5B4\uB098\uAE30 \uC804\uC758 \uD655\uB960\uC744 \uB73B\uD55C\uB2E4. \uB610\uB294 \uACBD\uACC4 \uD655\uB960, \uC120\uD5D8\uC801 \uD655\uB960\uC740 \uBCA0\uC774\uC988 \uCD94\uB860\uC5D0\uC11C \uAD00\uCE21\uC790\uAC00 \uAD00\uCE21\uC744 \uD558\uAE30 \uC804\uC5D0 \uAC00\uC9C0\uACE0 \uC788\uB294 \uD655\uB960 \uBD84\uD3EC\uB97C \uC758\uBBF8\uD55C\uB2E4. \uC0AC\uC804 \uD655\uB960\uACFC \uAC00\uB2A5\uB3C4\uAC00 \uC8FC\uC5B4\uC84C\uC744 \uB54C, \uAD00\uCE21\uC790\uB294 \uAD00\uCE21\uAC12\uC744 \uC5BB\uC740 \uB2E4\uC74C \uBCA0\uC774\uC988 \uC815\uB9AC\uC5D0 \uC758\uD574 \uC0AC\uD6C4 \uD655\uB960\uC744 \uC5BB\uC744 \uC218 \uC788\uB2E4. \uC0AC\uC804 \uD655\uB960\uC740 \uC77C\uBC18\uC801\uC73C\uB85C \uC2E4\uD5D8\uD558\uB294 \uB300\uC0C1\uC5D0 \uB300\uD574 \uC798 \uC54C\uACE0 \uC788\uB294 \uC804\uBB38\uAC00\uAC00 \uC120\uD0DD\uD558\uAC70\uB098(informative prior), \uD639\uC740 \uC804\uBB38\uC801\uC778 \uC815\uBCF4\uAC00 \uC5C6\uB294 \uBB34\uC815\uBCF4\uC801 \uBD84\uD3EC(uninformative prior)\uB85C \uC8FC\uC5B4\uC9C4\uB2E4. \uB610\uD55C \uD2B9\uC815\uD55C \uAC00\uB2A5\uB3C4 \uBD84\uD3EC\uC5D0 \uB300\uD574\uC11C\uB294 (conjugate prior)\uC744 \uC120\uD0DD\uD558\uC5EC \uACC4\uC0B0\uC801\uC778 \uC774\uC810\uC744 \uC5BB\uC744 \uC218 \uC788\uB2E4."@ko . . . . . . "Probabilidade a priori"@pt . . . . . . . "Prior probability"@en . . . "\u0412 \u0431\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u043E\u043C \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u043C \u0432\u044B\u0432\u043E\u0434\u0435 \u0430\u043F\u0440\u0438\u043E\u0440\u043D\u043E\u0435 \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0435\u0439 (\u0430\u043D\u0433\u043B. prior probability distribution, \u0438\u043B\u0438 \u043F\u0440\u043E\u0441\u0442\u043E prior) \u043D\u0435\u043E\u043F\u0440\u0435\u0434\u0435\u043B\u0451\u043D\u043D\u043E\u0439 \u0432\u0435\u043B\u0438\u0447\u0438\u043D\u044B \u2014 \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0435\u0439, \u043A\u043E\u0442\u043E\u0440\u043E\u0435 \u0432\u044B\u0440\u0430\u0436\u0430\u0435\u0442 \u043F\u0440\u0435\u0434\u043F\u043E\u043B\u043E\u0436\u0435\u043D\u0438\u044F \u043E \u0434\u043E \u0443\u0447\u0451\u0442\u0430 \u044D\u043A\u0441\u043F\u0435\u0440\u0438\u043C\u0435\u043D\u0442\u0430\u043B\u044C\u043D\u044B\u0445 \u0434\u0430\u043D\u043D\u044B\u0445. \u041D\u0430\u043F\u0440\u0438\u043C\u0435\u0440, \u0435\u0441\u043B\u0438 \u2014 \u0434\u043E\u043B\u044F \u0438\u0437\u0431\u0438\u0440\u0430\u0442\u0435\u043B\u0435\u0439, \u0433\u043E\u0442\u043E\u0432\u044B\u0445 \u0433\u043E\u043B\u043E\u0441\u043E\u0432\u0430\u0442\u044C \u0437\u0430 \u043E\u043F\u0440\u0435\u0434\u0435\u043B\u0451\u043D\u043D\u043E\u0433\u043E \u043A\u0430\u043D\u0434\u0438\u0434\u0430\u0442\u0430, \u0442\u043E \u0430\u043F\u0440\u0438\u043E\u0440\u043D\u044B\u043C \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435\u043C \u0431\u0443\u0434\u0435\u0442 \u043F\u0440\u0435\u0434\u043F\u043E\u043B\u043E\u0436\u0435\u043D\u0438\u0435 \u043E \u0434\u043E \u0443\u0447\u0451\u0442\u0430 \u0440\u0435\u0437\u0443\u043B\u044C\u0442\u0430\u0442\u043E\u0432 \u043E\u043F\u0440\u043E\u0441\u043E\u0432 \u0438\u043B\u0438 \u0432\u044B\u0431\u043E\u0440\u043E\u0432. \u041F\u0440\u043E\u0442\u0438\u0432\u043E\u043F\u043E\u0441\u0442\u0430\u0432\u043B\u044F\u0435\u0442\u0441\u044F \u0430\u043F\u043E\u0441\u0442\u0435\u0440\u0438\u043E\u0440\u043D\u043E\u0439 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0438."@ru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Probabilit\u00E9 a priori"@fr . . . . . . . "\u0423 \u0431\u0430\u0454\u0441\u043E\u0432\u043E\u043C\u0443 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043D\u043E\u043C\u0443 \u0432\u0438\u0441\u043D\u043E\u0432\u0443\u0432\u0430\u043D\u043D\u0456 \u0430\u043F\u0440\u0456\u043E\u0301\u0440\u043D\u0438\u0439 \u0440\u043E\u0437\u043F\u043E\u0301\u0434\u0456\u043B \u0439\u043C\u043E\u0432\u0456\u0301\u0440\u043D\u043E\u0441\u0442\u0456 (\u0430\u043D\u0433\u043B. prior probability distribution), \u0449\u043E \u0447\u0430\u0441\u0442\u043E \u043D\u0430\u0437\u0438\u0432\u0430\u044E\u0442\u044C \u043F\u0440\u043E\u0441\u0442\u043E \u0430\u043F\u0440\u0456\u043E\u0301\u0440\u043D\u0435 (\u0430\u043D\u0433\u043B. prior), \u0434\u0435\u044F\u043A\u043E\u0457 \u043D\u0435\u0432\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u043E\u0457 \u043A\u0456\u043B\u044C\u043A\u043E\u0441\u0442\u0456 \u2014 \u0446\u0435 \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B \u0439\u043C\u043E\u0432\u0456\u0440\u043D\u043E\u0441\u0442\u0456 p, \u0449\u043E \u0432\u0438\u0440\u0430\u0436\u0430\u0442\u0438\u043C\u0435 \u0447\u0438\u0454\u0441\u044C \u043F\u0435\u0440\u0435\u043A\u043E\u043D\u0430\u043D\u043D\u044F \u043F\u0440\u043E \u0446\u044E \u043A\u0456\u043B\u044C\u043A\u0456\u0441\u0442\u044C \u043F\u0435\u0440\u0435\u0434 \u0432\u0440\u0430\u0445\u0443\u0432\u0430\u043D\u043D\u044F\u043C \u044F\u043A\u043E\u0433\u043E\u0441\u044C \u0441\u0432\u0456\u0434\u0447\u0435\u043D\u043D\u044F. \u041D\u0430\u043F\u0440\u0438\u043A\u043B\u0430\u0434, p \u043C\u043E\u0436\u0435 \u0431\u0443\u0442\u0438 \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u043E\u043C \u0439\u043C\u043E\u0432\u0456\u0440\u043D\u043E\u0441\u0442\u0456 \u043F\u0440\u043E\u043F\u043E\u0440\u0446\u0456\u0457 \u0432\u0438\u0431\u043E\u0440\u0446\u0456\u0432, \u0449\u043E \u0433\u043E\u043B\u043E\u0441\u0443\u0432\u0430\u0442\u0438\u043C\u0443\u0442\u044C \u0437\u0430 \u043F\u0435\u0432\u043D\u043E\u0433\u043E \u043F\u043E\u043B\u0456\u0442\u0438\u043A\u0430 \u043D\u0430 \u043C\u0430\u0439\u0431\u0443\u0442\u043D\u0456\u0445 \u0432\u0438\u0431\u043E\u0440\u0430\u0445. \u0412\u0456\u043D \u043F\u0440\u0438\u043F\u0438\u0441\u0443\u0454 \u0446\u0456\u0439 \u043A\u0456\u043B\u044C\u043A\u043E\u0441\u0442\u0456 \u0448\u0432\u0438\u0434\u0448\u0435 \u043D\u0435\u0432\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u0456\u0441\u0442\u044C, \u043D\u0456\u0436 \u0432\u0438\u043F\u0430\u0434\u043A\u043E\u0432\u0456\u0441\u0442\u044C. \u0426\u044F \u043D\u0435\u0432\u0456\u0434\u043E\u043C\u0430 \u043A\u0456\u043B\u044C\u043A\u0456\u0441\u0442\u044C \u043C\u043E\u0436\u0435 \u0431\u0443\u0442\u0438 \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u043E\u043C \u0430\u0431\u043E \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E\u044E \u0437\u043C\u0456\u043D\u043D\u043E\u044E. \u0422\u0435\u043E\u0440\u0435\u043C\u0430 \u0411\u0430\u0454\u0441\u0430 \u0437\u0430\u0441\u0442\u043E\u0441\u043E\u0432\u0443\u0454\u0442\u044C\u0441\u044F \u0448\u043B\u044F\u0445\u043E\u043C \u043C\u043D\u043E\u0436\u0435\u043D\u043D\u044F \u0430\u043F\u0440\u0456\u043E\u0440\u043D\u043E\u0433\u043E \u043D\u0430 \u0444\u0443\u043D\u043A\u0446\u0456\u044E \u043F\u0440\u0430\u0432\u0434\u043E\u043F\u043E\u0434\u0456\u0431\u043D\u043E\u0441\u0442\u0456 \u0437 \u043D\u0430\u0441\u0442\u0443\u043F\u043D\u0438\u043C \u043D\u043E\u0440\u043C\u0443\u0432\u0430\u043D\u043D\u044F\u043C \u0434\u043B\u044F \u043E\u0442\u0440\u0438\u043C\u0430\u043D\u043D\u044F \u0430\u043F\u043E\u0441\u0442\u0435\u0440\u0456\u043E\u0440\u043D\u043E\u0433\u043E \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u0443 \u0439\u043C\u043E\u0432\u0456\u0440\u043D\u043E\u0441\u0442\u0456, \u0449\u043E \u0454 \u0443\u043C\u043E\u0432\u043D\u0438\u043C \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u043E\u043C \u0446\u0456\u0454\u0457 \u043D\u0435\u0432\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u043E\u0457 \u043A\u0456\u043B\u044C\u043A\u043E\u0441\u0442\u0456 \u0437 \u0443\u0440\u0430\u0445\u0443\u0432\u0430\u043D\u043D\u044F\u043C \u043E\u0442\u0440\u0438\u043C\u0430\u043D\u0438\u0445 \u0434\u0430\u043D\u0438\u0445. \u0410\u043F\u0440\u0456\u043E\u0440\u043D\u0435 \u0447\u0430\u0441\u0442\u043E \u0454 \u0447\u0438\u0441\u0442\u043E \u0441\u0443\u0431'\u0454\u043A\u0442\u0438\u0432\u043D\u043E\u044E \u043E\u0446\u0456\u043D\u043A\u043E\u044E \u0434\u043E\u0441\u0432\u0456\u0434\u0447\u0435\u043D\u043E\u0433\u043E \u0444\u0430\u0445\u0456\u0432\u0446\u044F. \u0414\u0435\u0445\u0442\u043E \u043F\u0440\u0438 \u043C\u043E\u0436\u043B\u0438\u0432\u043E\u0441\u0442\u0456 \u043E\u0431\u0438\u0440\u0430\u0442\u0438\u043C\u0435 \u0441\u043F\u0440\u044F\u0436\u0435\u043D\u0438\u0439 \u0430\u043F\u0440\u0456\u043E\u0440\u043D\u0438\u0439 \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B \u0434\u043B\u044F \u0441\u043F\u0440\u043E\u0449\u0435\u043D\u043D\u044F \u043E\u0431\u0447\u0438\u0441\u043B\u0435\u043D\u043D\u044F \u0430\u043F\u043E\u0441\u0442\u0435\u0440\u0456\u043E\u0440\u043D\u043E\u0433\u043E \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u0443. \u041F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u0438 \u0430\u043F\u0440\u0456\u043E\u0440\u043D\u0438\u0445 \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u0456\u0432 \u043D\u0430\u0437\u0438\u0432\u0430\u044E\u0442\u044C \u0433\u0456\u043F\u0435\u0440\u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u0430\u043C\u0438, \u0449\u043E\u0431\u0438 \u0432\u0456\u0434\u0440\u0456\u0437\u043D\u044F\u0442\u0438 \u0457\u0445 \u0432\u0456\u0434 \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u0456\u0432 \u043C\u043E\u0434\u0435\u043B\u0456 \u0431\u0430\u0437\u043E\u0432\u0438\u0445 \u0434\u0430\u043D\u0438\u0445. \u041D\u0430\u043F\u0440\u0438\u043A\u043B\u0430\u0434, \u044F\u043A\u0449\u043E \u0445\u0442\u043E\u0441\u044C \u0432\u0438\u043A\u043E\u0440\u0438\u0441\u0442\u043E\u0432\u0443\u0454 \u0431\u0435\u0442\u0430-\u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B \u0434\u043B\u044F \u043C\u043E\u0434\u0435\u043B\u044E\u0432\u0430\u043D\u043D\u044F \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u0443 \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u0430 p \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u0443 \u0411\u0435\u0440\u043D\u0443\u043B\u043B\u0456, \u0442\u043E: \n* p \u0454 \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u043E\u043C \u0431\u0430\u0437\u043E\u0432\u043E\u0457 \u0441\u0438\u0441\u0442\u0435\u043C\u0438 (\u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u0443 \u0411\u0435\u0440\u043D\u0443\u043B\u043B\u0456), \u0430 \n* \u03B1 \u0442\u0430 \u03B2 \u0454 \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u0430\u043C\u0438 \u0430\u043F\u0440\u0456\u043E\u0440\u043D\u043E\u0433\u043E \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u0443 (\u0431\u0435\u0442\u0430-\u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u0443), \u0442\u043E\u0431\u0442\u043E \u0433\u0456\u043F\u0435\u0440\u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u0430\u043C\u0438."@uk . . . . . . . . . . . . . . "Nell'ambito dell'inferenza statistica bayesiana, una distribuzione di probabilit\u00E0 a priori, detta spesso anche distribuzione a priori, di una quantit\u00E0 incognita p (per esempio, supponiamo p essere la proporzione di votanti che voteranno per il politico Rossi in un'elezione futura) \u00E8 la distribuzione di probabilit\u00E0 che esprimerebbe l'incertezza di p prima che i \"dati\" (per esempio, un sondaggio di opinione) siano presi in considerazione. Il proposito \u00E8 di attribuire incertezza piuttosto che casualit\u00E0 a una quantit\u00E0 incerta. La quantit\u00E0 incognita pu\u00F2 essere un parametro o una ."@it . "A-priori-Wahrscheinlichkeit"@de . . "\u0412 \u0431\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u043E\u043C \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u043C \u0432\u044B\u0432\u043E\u0434\u0435 \u0430\u043F\u0440\u0438\u043E\u0440\u043D\u043E\u0435 \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0435\u0439 (\u0430\u043D\u0433\u043B. prior probability distribution, \u0438\u043B\u0438 \u043F\u0440\u043E\u0441\u0442\u043E prior) \u043D\u0435\u043E\u043F\u0440\u0435\u0434\u0435\u043B\u0451\u043D\u043D\u043E\u0439 \u0432\u0435\u043B\u0438\u0447\u0438\u043D\u044B \u2014 \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0435\u0439, \u043A\u043E\u0442\u043E\u0440\u043E\u0435 \u0432\u044B\u0440\u0430\u0436\u0430\u0435\u0442 \u043F\u0440\u0435\u0434\u043F\u043E\u043B\u043E\u0436\u0435\u043D\u0438\u044F \u043E \u0434\u043E \u0443\u0447\u0451\u0442\u0430 \u044D\u043A\u0441\u043F\u0435\u0440\u0438\u043C\u0435\u043D\u0442\u0430\u043B\u044C\u043D\u044B\u0445 \u0434\u0430\u043D\u043D\u044B\u0445. \u041D\u0430\u043F\u0440\u0438\u043C\u0435\u0440, \u0435\u0441\u043B\u0438 \u2014 \u0434\u043E\u043B\u044F \u0438\u0437\u0431\u0438\u0440\u0430\u0442\u0435\u043B\u0435\u0439, \u0433\u043E\u0442\u043E\u0432\u044B\u0445 \u0433\u043E\u043B\u043E\u0441\u043E\u0432\u0430\u0442\u044C \u0437\u0430 \u043E\u043F\u0440\u0435\u0434\u0435\u043B\u0451\u043D\u043D\u043E\u0433\u043E \u043A\u0430\u043D\u0434\u0438\u0434\u0430\u0442\u0430, \u0442\u043E \u0430\u043F\u0440\u0438\u043E\u0440\u043D\u044B\u043C \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435\u043C \u0431\u0443\u0434\u0435\u0442 \u043F\u0440\u0435\u0434\u043F\u043E\u043B\u043E\u0436\u0435\u043D\u0438\u0435 \u043E \u0434\u043E \u0443\u0447\u0451\u0442\u0430 \u0440\u0435\u0437\u0443\u043B\u044C\u0442\u0430\u0442\u043E\u0432 \u043E\u043F\u0440\u043E\u0441\u043E\u0432 \u0438\u043B\u0438 \u0432\u044B\u0431\u043E\u0440\u043E\u0432. \u041F\u0440\u043E\u0442\u0438\u0432\u043E\u043F\u043E\u0441\u0442\u0430\u0432\u043B\u044F\u0435\u0442\u0441\u044F \u0430\u043F\u043E\u0441\u0442\u0435\u0440\u0438\u043E\u0440\u043D\u043E\u0439 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0438. \u0421\u043E\u0433\u043B\u0430\u0441\u043D\u043E \u0442\u0435\u043E\u0440\u0435\u043C\u0435 \u0411\u0430\u0439\u0435\u0441\u0430, \u043D\u043E\u0440\u043C\u0430\u043B\u0438\u0437\u043E\u0432\u0430\u043D\u043D\u043E\u0435 \u043F\u0440\u043E\u0438\u0437\u0432\u0435\u0434\u0435\u043D\u0438\u0435 \u0430\u043F\u0440\u0438\u043E\u0440\u043D\u043E\u0433\u043E \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u044F \u043D\u0430 \u0444\u0443\u043D\u043A\u0446\u0438\u044E \u043F\u0440\u0430\u0432\u0434\u043E\u043F\u043E\u0434\u043E\u0431\u0438\u044F \u044F\u0432\u043B\u044F\u0435\u0442\u0441\u044F \u0443\u0441\u043B\u043E\u0432\u043D\u044B\u043C \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435\u043C \u043D\u0435\u043E\u043F\u0440\u0435\u0434\u0435\u043B\u0451\u043D\u043D\u043E\u0439 \u0432\u0435\u043B\u0438\u0447\u0438\u043D\u044B \u0441\u043E\u0433\u043B\u0430\u0441\u043D\u043E \u0443\u0447\u0442\u0451\u043D\u043D\u044B\u043C \u0434\u0430\u043D\u043D\u044B\u043C. \u0410\u043F\u0440\u0438\u043E\u0440\u043D\u043E\u0435 \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435 \u0447\u0430\u0441\u0442\u043E \u0437\u0430\u0434\u0430\u0435\u0442\u0441\u044F \u0441\u0443\u0431\u044A\u0435\u043A\u0442\u0438\u0432\u043D\u043E \u043E\u043F\u044B\u0442\u043D\u044B\u043C \u044D\u043A\u0441\u043F\u0435\u0440\u0442\u043E\u043C. \u041F\u0440\u0438 \u0432\u043E\u0437\u043C\u043E\u0436\u043D\u043E\u0441\u0442\u0438 \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u0443\u044E\u0442 \u0441\u043E\u043F\u0440\u044F\u0436\u0451\u043D\u043D\u043E\u0435 \u0430\u043F\u0440\u0438\u043E\u0440\u043D\u043E\u0435 \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435, \u0447\u0442\u043E \u0443\u043F\u0440\u043E\u0449\u0430\u0435\u0442 \u0432\u044B\u0447\u0438\u0441\u043B\u0435\u043D\u0438\u044F. \u041F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u044B \u0430\u043F\u0440\u0438\u043E\u0440\u043D\u043E\u0433\u043E \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u044F \u043D\u0430\u0437\u044B\u0432\u0430\u044E\u0442 , \u0447\u0442\u043E\u0431\u044B \u043E\u0442\u043B\u0438\u0447\u0438\u0442\u044C \u0438\u0445 \u043E\u0442 \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u043E\u0432 \u043C\u043E\u0434\u0435\u043B\u0438 \u0434\u0430\u043D\u043D\u044B\u0445. \u041D\u0430\u043F\u0440\u0438\u043C\u0435\u0440, \u0435\u0441\u043B\u0438 \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u0443\u0435\u0442\u0441\u044F \u0431\u0435\u0442\u0430-\u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u0435 \u0434\u043B\u044F \u043C\u043E\u0434\u0435\u043B\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u044F \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u0430 \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u044F \u0411\u0435\u0440\u043D\u0443\u043B\u043B\u0438, \u0442\u043E: \n* \u2014 \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440 \u043C\u043E\u0434\u0435\u043B\u0438 \u0434\u0430\u043D\u043D\u044B\u0445 (\u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u044F \u0411\u0435\u0440\u043D\u0443\u043B\u043B\u0438); \n* \u0438 \u2014 \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u044B \u0430\u043F\u0440\u0438\u043E\u0440\u043D\u043E\u0433\u043E \u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u044F (\u0431\u0435\u0442\u0430-\u0440\u0430\u0441\u043F\u0440\u0435\u0434\u0435\u043B\u0435\u043D\u0438\u044F), \u0442\u043E \u0435\u0441\u0442\u044C \u0433\u0438\u043F\u0435\u0440\u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u044B."@ru . . . . . "A Jeffreys prior is related to KL divergence?"@en . . "\u0410\u043F\u0440\u0456\u043E\u0440\u043D\u0430 \u0439\u043C\u043E\u0432\u0456\u0440\u043D\u0456\u0441\u0442\u044C"@uk . . . . . . . . . . . . . . . . . . . . . "\u5728\u8D1D\u53F6\u65AF\u7EDF\u8BA1\u4E2D\uFF0C\u67D0\u4E00\u4E0D\u786E\u5B9A\u91CFp\u7684\u5148\u9A8C\u6982\u7387\uFF08Prior probability\uFF09\u5206\u5E03\u662F\u5728\u8003\u8651\u300C\u89C2\u6D4B\u6570\u636E\u300D\u524D\uFF0C\u80FD\u8868\u8FBEp\u4E0D\u786E\u5B9A\u6027\u7684\u6982\u7387\u5206\u5E03\u3002\u5B83\u65E8\u5728\u63CF\u8FF0\u8FD9\u4E2A\u4E0D\u786E\u5B9A\u91CF\u7684\u4E0D\u786E\u5B9A\u7A0B\u5EA6\uFF0C\u800C\u4E0D\u662F\u8FD9\u4E2A\u4E0D\u786E\u5B9A\u91CF\u7684\u968F\u673A\u6027\u3002\u8FD9\u4E2A\u4E0D\u786E\u5B9A\u91CF\u53EF\u4EE5\u662F\u4E00\u4E2A\u53C2\u6570\uFF0C\u6216\u8005\u662F\u4E00\u4E2A\u9690\u542B\u53D8\u91CF\uFF08\u82F1\u8A9E\uFF1Alatent variable\uFF09\u3002\u4F9D\u64DA\u61C9\u7528\u9818\u57DF\u7684\u4E0D\u540C\uFF0C\u4E8B\u524D\u6A5F\u7387\u53C8\u53EB\u505A\u5148\u9A57\u6A5F\u7387\u3001\u5148\u9A57\u6982\u7387\u3001\u4E8B\u524D\u5148\u9A57\u6A5F\u7387\u3001\u5C45\u5148\u6A5F\u7387\u3002 \u5728\u4F7F\u7528\u8D1D\u53F6\u65AF\u5B9A\u7406\u65F6\uFF0C\u6211\u4EEC\u901A\u8FC7\u5C06\u5148\u9A8C\u6982\u7387\u4E0E\u4F3C\u7136\u51FD\u6570\u76F8\u4E58\uFF0C\u968F\u540E\u6807\u51C6\u5316\uFF0C\u6765\u5F97\u5230\u540E\u9A8C\u6982\u7387\u5206\u5E03\uFF0C\u4E5F\u5C31\u662F\u7ED9\u51FA\u67D0\u6570\u636E\uFF0C\u8BE5\u4E0D\u786E\u5B9A\u91CF\u7684\u6761\u4EF6\u5206\u5E03\u3002 \u5148\u9A8C\u6982\u7387\u901A\u5E38\u662F\u4E3B\u89C2\u7684\u731C\u6D4B\uFF0C\u4E3A\u4E86\u4F7F\u8BA1\u7B97\u540E\u9A8C\u6982\u7387\u65B9\u4FBF\uFF0C\u6709\u65F6\u5019\u4F1A\u9009\u62E9\u5171\u8F6D\u5148\u9A8C\u3002\u5982\u679C\u540E\u9A8C\u6982\u7387\u548C\u5148\u9A8C\u6982\u7387\u662F\u540C\u4E00\u65CF\u7684\uFF0C\u5219\u8BA4\u4E3A\u5B83\u4EEC\u662F\u5171\u8F6D\u5206\u5E03\uFF0C\u8FD9\u4E2A\u5148\u9A8C\u6982\u7387\u5C31\u662F\u5BF9\u5E94\u4E8E\u4F3C\u7136\u51FD\u6570\u7684\u5171\u8F6D\u5148\u9A8C\u3002"@zh . . . . . . . . . "Em probabilidade bayesiana, uma distribui\u00E7\u00E3o de probabilidade a priori para uma quantidade indeterminada p, tamb\u00E9m chamada simplesmente de prior relativo a p (suponha, por exemplo, que p seja a propor\u00E7\u00E3o de votantes em determinado pol\u00EDtico numa elei\u00E7\u00E3o futura) \u00E9 a distribui\u00E7\u00E3o de probabilidade que expressaria a incerteza sobre o valor de p antes de qualquer dado ou medida (por exemplo, uma pesquisa de opini\u00E3o). \u00C9 uma maneira de atribuir incerteza em vez de aleatoriedade \u00E0 grandeza em quest\u00E3o, al\u00E9m de ponto de partida para o uso do teorema de Bayes ap\u00F3s a obten\u00E7\u00E3o dos dados."@pt . "En inferencia estad\u00EDstica Bayesiana, una distribuci\u00F3n de probabilidad a priori de una cantidad p desconocida, es la distribuci\u00F3n de probabilidad que expresa alguna incertidumbre acerca de p antes de tomar en cuenta los datos. Aplicando el Teorema de Bayes, la probabilidad a priori se multiplica por la verosimilitud; al normalizar se obtiene la distribuci\u00F3n de probabilidad a posteriori, la cual es la probabilidad de la distribuci\u00F3n condicional dados los datos."@es . . . . . . . . . . "\u0423 \u0431\u0430\u0454\u0441\u043E\u0432\u043E\u043C\u0443 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043D\u043E\u043C\u0443 \u0432\u0438\u0441\u043D\u043E\u0432\u0443\u0432\u0430\u043D\u043D\u0456 \u0430\u043F\u0440\u0456\u043E\u0301\u0440\u043D\u0438\u0439 \u0440\u043E\u0437\u043F\u043E\u0301\u0434\u0456\u043B \u0439\u043C\u043E\u0432\u0456\u0301\u0440\u043D\u043E\u0441\u0442\u0456 (\u0430\u043D\u0433\u043B. prior probability distribution), \u0449\u043E \u0447\u0430\u0441\u0442\u043E \u043D\u0430\u0437\u0438\u0432\u0430\u044E\u0442\u044C \u043F\u0440\u043E\u0441\u0442\u043E \u0430\u043F\u0440\u0456\u043E\u0301\u0440\u043D\u0435 (\u0430\u043D\u0433\u043B. prior), \u0434\u0435\u044F\u043A\u043E\u0457 \u043D\u0435\u0432\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u043E\u0457 \u043A\u0456\u043B\u044C\u043A\u043E\u0441\u0442\u0456 \u2014 \u0446\u0435 \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B \u0439\u043C\u043E\u0432\u0456\u0440\u043D\u043E\u0441\u0442\u0456 p, \u0449\u043E \u0432\u0438\u0440\u0430\u0436\u0430\u0442\u0438\u043C\u0435 \u0447\u0438\u0454\u0441\u044C \u043F\u0435\u0440\u0435\u043A\u043E\u043D\u0430\u043D\u043D\u044F \u043F\u0440\u043E \u0446\u044E \u043A\u0456\u043B\u044C\u043A\u0456\u0441\u0442\u044C \u043F\u0435\u0440\u0435\u0434 \u0432\u0440\u0430\u0445\u0443\u0432\u0430\u043D\u043D\u044F\u043C \u044F\u043A\u043E\u0433\u043E\u0441\u044C \u0441\u0432\u0456\u0434\u0447\u0435\u043D\u043D\u044F. \u041D\u0430\u043F\u0440\u0438\u043A\u043B\u0430\u0434, p \u043C\u043E\u0436\u0435 \u0431\u0443\u0442\u0438 \u0440\u043E\u0437\u043F\u043E\u0434\u0456\u043B\u043E\u043C \u0439\u043C\u043E\u0432\u0456\u0440\u043D\u043E\u0441\u0442\u0456 \u043F\u0440\u043E\u043F\u043E\u0440\u0446\u0456\u0457 \u0432\u0438\u0431\u043E\u0440\u0446\u0456\u0432, \u0449\u043E \u0433\u043E\u043B\u043E\u0441\u0443\u0432\u0430\u0442\u0438\u043C\u0443\u0442\u044C \u0437\u0430 \u043F\u0435\u0432\u043D\u043E\u0433\u043E \u043F\u043E\u043B\u0456\u0442\u0438\u043A\u0430 \u043D\u0430 \u043C\u0430\u0439\u0431\u0443\u0442\u043D\u0456\u0445 \u0432\u0438\u0431\u043E\u0440\u0430\u0445. \u0412\u0456\u043D \u043F\u0440\u0438\u043F\u0438\u0441\u0443\u0454 \u0446\u0456\u0439 \u043A\u0456\u043B\u044C\u043A\u043E\u0441\u0442\u0456 \u0448\u0432\u0438\u0434\u0448\u0435 \u043D\u0435\u0432\u0438\u0437\u043D\u0430\u0447\u0435\u043D\u0456\u0441\u0442\u044C, \u043D\u0456\u0436 \u0432\u0438\u043F\u0430\u0434\u043A\u043E\u0432\u0456\u0441\u0442\u044C. \u0426\u044F \u043D\u0435\u0432\u0456\u0434\u043E\u043C\u0430 \u043A\u0456\u043B\u044C\u043A\u0456\u0441\u0442\u044C \u043C\u043E\u0436\u0435 \u0431\u0443\u0442\u0438 \u043F\u0430\u0440\u0430\u043C\u0435\u0442\u0440\u043E\u043C \u0430\u0431\u043E \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E\u044E \u0437\u043C\u0456\u043D\u043D\u043E\u044E."@uk . . . . . . . . . . . . . . . . . . . "\u4E8B\u524D\u78BA\u7387"@ja . . . . . "1102577752"^^ . . . . . . . "Nell'ambito dell'inferenza statistica bayesiana, una distribuzione di probabilit\u00E0 a priori, detta spesso anche distribuzione a priori, di una quantit\u00E0 incognita p (per esempio, supponiamo p essere la proporzione di votanti che voteranno per il politico Rossi in un'elezione futura) \u00E8 la distribuzione di probabilit\u00E0 che esprimerebbe l'incertezza di p prima che i \"dati\" (per esempio, un sondaggio di opinione) siano presi in considerazione. Il proposito \u00E8 di attribuire incertezza piuttosto che casualit\u00E0 a una quantit\u00E0 incerta. La quantit\u00E0 incognita pu\u00F2 essere un parametro o una . Si applica il teorema di Bayes, moltiplicando la distribuzione a priori per la funzione di verosimiglianza e quindi normalizzando, per ottenere la , la quale \u00E8 la distribuzione condizionata della quantit\u00E0 incerta una volta ottenuti i dati. Spesso una distribuzione a priori \u00E8 l'accertamento soggettivo (elicitazione) di una persona esperta. Quando possibile, alcuni sceglieranno una distribuzione a priori coniugata per rendere pi\u00F9 semplice il calcolo della distribuzione a posteriori. I parametri di una distribuzione a priori sono chiamati iperparametri, per distinguerli dai parametri del modello dei dati sottostanti. Per esempio, se si sta usando una distribuzione beta per modellare la distribuzione di un parametro p di una distribuzione di Bernoulli, allora: \n* p \u00E8 un parametro del(la distribuzione di Bernoulli del) sistema sottostante, e \n* \u03B1 e \u03B2 sono parametri della distribuzione a priori (distribuzione beta), quindi sono iperparametri."@it . "Distribuzione di probabilit\u00E0 a priori"@it . . . . . . . . . . . . . . . "counterexample of what?"@en . . . . . . . . "\uC0AC\uC804 \uD655\uB960"@ko . . . "\u5728\u8D1D\u53F6\u65AF\u7EDF\u8BA1\u4E2D\uFF0C\u67D0\u4E00\u4E0D\u786E\u5B9A\u91CFp\u7684\u5148\u9A8C\u6982\u7387\uFF08Prior probability\uFF09\u5206\u5E03\u662F\u5728\u8003\u8651\u300C\u89C2\u6D4B\u6570\u636E\u300D\u524D\uFF0C\u80FD\u8868\u8FBEp\u4E0D\u786E\u5B9A\u6027\u7684\u6982\u7387\u5206\u5E03\u3002\u5B83\u65E8\u5728\u63CF\u8FF0\u8FD9\u4E2A\u4E0D\u786E\u5B9A\u91CF\u7684\u4E0D\u786E\u5B9A\u7A0B\u5EA6\uFF0C\u800C\u4E0D\u662F\u8FD9\u4E2A\u4E0D\u786E\u5B9A\u91CF\u7684\u968F\u673A\u6027\u3002\u8FD9\u4E2A\u4E0D\u786E\u5B9A\u91CF\u53EF\u4EE5\u662F\u4E00\u4E2A\u53C2\u6570\uFF0C\u6216\u8005\u662F\u4E00\u4E2A\u9690\u542B\u53D8\u91CF\uFF08\u82F1\u8A9E\uFF1Alatent variable\uFF09\u3002\u4F9D\u64DA\u61C9\u7528\u9818\u57DF\u7684\u4E0D\u540C\uFF0C\u4E8B\u524D\u6A5F\u7387\u53C8\u53EB\u505A\u5148\u9A57\u6A5F\u7387\u3001\u5148\u9A57\u6982\u7387\u3001\u4E8B\u524D\u5148\u9A57\u6A5F\u7387\u3001\u5C45\u5148\u6A5F\u7387\u3002 \u5728\u4F7F\u7528\u8D1D\u53F6\u65AF\u5B9A\u7406\u65F6\uFF0C\u6211\u4EEC\u901A\u8FC7\u5C06\u5148\u9A8C\u6982\u7387\u4E0E\u4F3C\u7136\u51FD\u6570\u76F8\u4E58\uFF0C\u968F\u540E\u6807\u51C6\u5316\uFF0C\u6765\u5F97\u5230\u540E\u9A8C\u6982\u7387\u5206\u5E03\uFF0C\u4E5F\u5C31\u662F\u7ED9\u51FA\u67D0\u6570\u636E\uFF0C\u8BE5\u4E0D\u786E\u5B9A\u91CF\u7684\u6761\u4EF6\u5206\u5E03\u3002 \u5148\u9A8C\u6982\u7387\u901A\u5E38\u662F\u4E3B\u89C2\u7684\u731C\u6D4B\uFF0C\u4E3A\u4E86\u4F7F\u8BA1\u7B97\u540E\u9A8C\u6982\u7387\u65B9\u4FBF\uFF0C\u6709\u65F6\u5019\u4F1A\u9009\u62E9\u5171\u8F6D\u5148\u9A8C\u3002\u5982\u679C\u540E\u9A8C\u6982\u7387\u548C\u5148\u9A8C\u6982\u7387\u662F\u540C\u4E00\u65CF\u7684\uFF0C\u5219\u8BA4\u4E3A\u5B83\u4EEC\u662F\u5171\u8F6D\u5206\u5E03\uFF0C\u8FD9\u4E2A\u5148\u9A8C\u6982\u7387\u5C31\u662F\u5BF9\u5E94\u4E8E\u4F3C\u7136\u51FD\u6570\u7684\u5171\u8F6D\u5148\u9A8C\u3002"@zh . "Dans le th\u00E9or\u00E8me de Bayes, la probabilit\u00E9 a priori (ou prior) d\u00E9signe une probabilit\u00E9 se fondant sur des donn\u00E9es ou connaissances ant\u00E9rieures \u00E0 une observation.Elle s'oppose \u00E0 la probabilit\u00E9 a posteriori (ou posterior) correspondante qui s'appuie sur les connaissances post\u00E9rieures \u00E0 cette observation."@fr . . . . . . . . . . . . "Probabilitat pr\u00E8via"@ca . . . . . . . "En inferencia estad\u00EDstica Bayesiana, una distribuci\u00F3n de probabilidad a priori de una cantidad p desconocida, es la distribuci\u00F3n de probabilidad que expresa alguna incertidumbre acerca de p antes de tomar en cuenta los datos. Aplicando el Teorema de Bayes, la probabilidad a priori se multiplica por la verosimilitud; al normalizar se obtiene la distribuci\u00F3n de probabilidad a posteriori, la cual es la probabilidad de la distribuci\u00F3n condicional dados los datos. Los par\u00E1metros de las distribuciones a priori son llamados , para distinguirlos de los par\u00E1metros del modelo. Por ejemplo, si se est\u00E1 usando una distribuci\u00F3n beta para modelar la distribuci\u00F3n del par\u00E1metro p, entonces: \n* p es un par\u00E1metro de una distribuci\u00F3n Bernoulli, y \n* \u03B1 y \u03B2 son par\u00E1metros de la distribuci\u00F3n a priori (distribuci\u00F3n beta), y por lo tanto hiperpar\u00E1metros. Una distribuci\u00F3n de probabilidad a priori informativa expresa informaci\u00F3n especifica y definida acerca de una variable. Una distribuci\u00F3n de probabilidad a priori no informativa expresa informaci\u00F3n general acerca de una variable."@es . "Em probabilidade bayesiana, uma distribui\u00E7\u00E3o de probabilidade a priori para uma quantidade indeterminada p, tamb\u00E9m chamada simplesmente de prior relativo a p (suponha, por exemplo, que p seja a propor\u00E7\u00E3o de votantes em determinado pol\u00EDtico numa elei\u00E7\u00E3o futura) \u00E9 a distribui\u00E7\u00E3o de probabilidade que expressaria a incerteza sobre o valor de p antes de qualquer dado ou medida (por exemplo, uma pesquisa de opini\u00E3o). \u00C9 uma maneira de atribuir incerteza em vez de aleatoriedade \u00E0 grandeza em quest\u00E3o, al\u00E9m de ponto de partida para o uso do teorema de Bayes ap\u00F3s a obten\u00E7\u00E3o dos dados."@pt . "In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account. For example, the prior could be the probability distribution representing the relative proportions of voters who will vote for a particular politician in a future election. The unknown quantity may be a parameter of the model or a latent variable rather than an observable variable."@en . . "September 2015"@en . . . "\u4E8B\u524D\u78BA\u7387\uFF08\u3058\u305C\u3093\u304B\u304F\u308A\u3064\u3001\u82F1: prior probability\uFF09\u3068\u306F\u6761\u4EF6\u4ED8\u304D\u78BA\u7387\u306E\u4E00\u7A2E\u3067\u3001\u8A3C\u62E0\u304C\u306A\u3044\u6761\u4EF6\u3067\u3001\u3042\u308B\u5909\u6570\u306B\u3064\u3044\u3066\u77E5\u3089\u308C\u3066\u3044\u308B\u3053\u3068\u3092\u78BA\u7387\u3068\u3057\u3066\u8868\u73FE\u3059\u308B\u3082\u306E\u3067\u3042\u308B\u3002\u5148\u9A13\u78BA\u7387\uFF08\u305B\u3093\u3051\u3093\u304B\u304F\u308A\u3064\uFF09\u3001\u30A2\u30D7\u30EA\u30AA\u30EA\u78BA\u7387\u3068\u3082\u3044\u3046\u3002 \u5BFE\u306B\u306A\u308B\u7528\u8A9E\u304C\u4E8B\u5F8C\u78BA\u7387\u3067\u3001\u3053\u308C\u306F\u8A3C\u62E0\u3092\u8003\u616E\u306B\u5165\u308C\u305F\u6761\u4EF6\u3067\u306E\u5909\u6570\u306E\u6761\u4EF6\u4ED8\u304D\u78BA\u7387\u3067\u3042\u308B\u3002\u4E8B\u5F8C\u78BA\u7387\u306F\u30D9\u30A4\u30BA\u306E\u5B9A\u7406\u306B\u3088\u308A\u3001\u4E8B\u524D\u78BA\u7387\u306B\u5C24\u5EA6\u95A2\u6570\u3092\u639B\u3051\u3066\u5F97\u3089\u308C\u308B\u3002 \u4E8B\u524D\u78BA\u7387\u3068\u4E8B\u5F8C\u78BA\u7387\u306F\u3001\u5F93\u6765\u306E\u983B\u5EA6\u4E3B\u7FA9\u7D71\u8A08\u5B66\u3067\u306F\u7528\u3044\u3089\u308C\u306A\u3044\u3001\u30D9\u30A4\u30BA\u7D71\u8A08\u5B66\u306E\u7528\u8A9E\u3067\u3042\u308B\u3002\u306A\u304A\u672C\u9805\u3067\u306F\u300C\u5909\u6570\u300D\u3068\u3044\u3046\u7528\u8A9E\u3092\u3001\u89B3\u6E2C\u3067\u304D\u308B\u78BA\u7387\u5909\u6570\u306E\u307B\u304B\u306B\u3001\u89B3\u6E2C\u3067\u304D\u306A\u3044\uFF08\u96A0\u308C\u305F\uFF09\u5909\u6570\u3001\u6BCD\u6570\u3042\u308B\u3044\u306F\u4EEE\u8AAC\u3082\u542B\u3081\u3066\u7528\u3044\u3066\u3044\u308B\u3002"@ja . . . . . . . . . . . "\u0410\u043F\u0440\u0438\u043E\u0440\u043D\u0430\u044F \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u044C"@ru . . . . . . . "472877"^^ . . . . . . . . . "\u4E8B\u524D\u78BA\u7387\uFF08\u3058\u305C\u3093\u304B\u304F\u308A\u3064\u3001\u82F1: prior probability\uFF09\u3068\u306F\u6761\u4EF6\u4ED8\u304D\u78BA\u7387\u306E\u4E00\u7A2E\u3067\u3001\u8A3C\u62E0\u304C\u306A\u3044\u6761\u4EF6\u3067\u3001\u3042\u308B\u5909\u6570\u306B\u3064\u3044\u3066\u77E5\u3089\u308C\u3066\u3044\u308B\u3053\u3068\u3092\u78BA\u7387\u3068\u3057\u3066\u8868\u73FE\u3059\u308B\u3082\u306E\u3067\u3042\u308B\u3002\u5148\u9A13\u78BA\u7387\uFF08\u305B\u3093\u3051\u3093\u304B\u304F\u308A\u3064\uFF09\u3001\u30A2\u30D7\u30EA\u30AA\u30EA\u78BA\u7387\u3068\u3082\u3044\u3046\u3002 \u5BFE\u306B\u306A\u308B\u7528\u8A9E\u304C\u4E8B\u5F8C\u78BA\u7387\u3067\u3001\u3053\u308C\u306F\u8A3C\u62E0\u3092\u8003\u616E\u306B\u5165\u308C\u305F\u6761\u4EF6\u3067\u306E\u5909\u6570\u306E\u6761\u4EF6\u4ED8\u304D\u78BA\u7387\u3067\u3042\u308B\u3002\u4E8B\u5F8C\u78BA\u7387\u306F\u30D9\u30A4\u30BA\u306E\u5B9A\u7406\u306B\u3088\u308A\u3001\u4E8B\u524D\u78BA\u7387\u306B\u5C24\u5EA6\u95A2\u6570\u3092\u639B\u3051\u3066\u5F97\u3089\u308C\u308B\u3002 \u4E8B\u524D\u78BA\u7387\u3068\u4E8B\u5F8C\u78BA\u7387\u306F\u3001\u5F93\u6765\u306E\u983B\u5EA6\u4E3B\u7FA9\u7D71\u8A08\u5B66\u3067\u306F\u7528\u3044\u3089\u308C\u306A\u3044\u3001\u30D9\u30A4\u30BA\u7D71\u8A08\u5B66\u306E\u7528\u8A9E\u3067\u3042\u308B\u3002\u306A\u304A\u672C\u9805\u3067\u306F\u300C\u5909\u6570\u300D\u3068\u3044\u3046\u7528\u8A9E\u3092\u3001\u89B3\u6E2C\u3067\u304D\u308B\u78BA\u7387\u5909\u6570\u306E\u307B\u304B\u306B\u3001\u89B3\u6E2C\u3067\u304D\u306A\u3044\uFF08\u96A0\u308C\u305F\uFF09\u5909\u6570\u3001\u6BCD\u6570\u3042\u308B\u3044\u306F\u4EEE\u8AAC\u3082\u542B\u3081\u3066\u7528\u3044\u3066\u3044\u308B\u3002"@ja . . . . . . . . . "\uC0AC\uC804 \uD655\uB960(\u4E8B\u524D\u78BA\u7387, \uC601\uC5B4: prior probability)\uC740 \uD2B9\uC815 \uC0AC\uC0C1\uC774 \uC77C\uC5B4\uB098\uAE30 \uC804\uC758 \uD655\uB960\uC744 \uB73B\uD55C\uB2E4. \uB610\uB294 \uACBD\uACC4 \uD655\uB960, \uC120\uD5D8\uC801 \uD655\uB960\uC740 \uBCA0\uC774\uC988 \uCD94\uB860\uC5D0\uC11C \uAD00\uCE21\uC790\uAC00 \uAD00\uCE21\uC744 \uD558\uAE30 \uC804\uC5D0 \uAC00\uC9C0\uACE0 \uC788\uB294 \uD655\uB960 \uBD84\uD3EC\uB97C \uC758\uBBF8\uD55C\uB2E4. \uC0AC\uC804 \uD655\uB960\uACFC \uAC00\uB2A5\uB3C4\uAC00 \uC8FC\uC5B4\uC84C\uC744 \uB54C, \uAD00\uCE21\uC790\uB294 \uAD00\uCE21\uAC12\uC744 \uC5BB\uC740 \uB2E4\uC74C \uBCA0\uC774\uC988 \uC815\uB9AC\uC5D0 \uC758\uD574 \uC0AC\uD6C4 \uD655\uB960\uC744 \uC5BB\uC744 \uC218 \uC788\uB2E4. \uC0AC\uC804 \uD655\uB960\uC740 \uC77C\uBC18\uC801\uC73C\uB85C \uC2E4\uD5D8\uD558\uB294 \uB300\uC0C1\uC5D0 \uB300\uD574 \uC798 \uC54C\uACE0 \uC788\uB294 \uC804\uBB38\uAC00\uAC00 \uC120\uD0DD\uD558\uAC70\uB098(informative prior), \uD639\uC740 \uC804\uBB38\uC801\uC778 \uC815\uBCF4\uAC00 \uC5C6\uB294 \uBB34\uC815\uBCF4\uC801 \uBD84\uD3EC(uninformative prior)\uB85C \uC8FC\uC5B4\uC9C4\uB2E4. \uB610\uD55C \uD2B9\uC815\uD55C \uAC00\uB2A5\uB3C4 \uBD84\uD3EC\uC5D0 \uB300\uD574\uC11C\uB294 (conjugate prior)\uC744 \uC120\uD0DD\uD558\uC5EC \uACC4\uC0B0\uC801\uC778 \uC774\uC810\uC744 \uC5BB\uC744 \uC218 \uC788\uB2E4."@ko . . . . . "In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account. For example, the prior could be the probability distribution representing the relative proportions of voters who will vote for a particular politician in a future election. The unknown quantity may be a parameter of the model or a latent variable rather than an observable variable. Bayes' theorem calculates the renormalized pointwise product of the prior and the likelihood function, to produce the posterior probability distribution, which is the conditional distribution of the uncertain quantity given the data. Similarly, the prior probability of a random event or an uncertain proposition is the unconditional probability that is assigned before any relevant evidence is taken into account. Priors can be created using a number of methods. A prior can be determined from past information, such as previous experiments. A prior can be elicited from the purely subjective assessment of an experienced expert. An uninformative prior can be created to reflect a balance among outcomes when no information is available. Priors can also be chosen according to some principle, such as symmetry or maximizing entropy given constraints; examples are the Jeffreys prior or Bernardo's reference prior. When a family of conjugate priors exists, choosing a prior from that family simplifies calculation of the posterior distribution. Parameters of prior distributions are a kind of hyperparameter. For example, if one uses a beta distribution to model the distribution of the parameter p of a Bernoulli distribution, then: \n* p is a parameter of the underlying system (Bernoulli distribution), and \n* \u03B1 and \u03B2 are parameters of the prior distribution (beta distribution); hence hyperparameters. Hyperparameters themselves may have hyperprior distributions expressing beliefs about their values. A Bayesian model with more than one level of prior like this is called a hierarchical Bayes model."@en . . . . . "Dans le th\u00E9or\u00E8me de Bayes, la probabilit\u00E9 a priori (ou prior) d\u00E9signe une probabilit\u00E9 se fondant sur des donn\u00E9es ou connaissances ant\u00E9rieures \u00E0 une observation.Elle s'oppose \u00E0 la probabilit\u00E9 a posteriori (ou posterior) correspondante qui s'appuie sur les connaissances post\u00E9rieures \u00E0 cette observation."@fr . . "Probabilidad a priori"@es . . . . . "May 2011"@en . . .