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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク DeGroot learning ： ウィキペディア英語版 DeGroot learning DeGroot learning refers to a rule-of-thumb type of social learning process. The idea was stated in its general form by the American statistician Morris H. DeGroot;〔 antecedents were articulated by John R. P. French〔 and Frank Harary.〔 The model has been used in physics, computer science and most widely in the theory of social networks.〔 == Setup and the learning process == Take a society of $n$ agents where everybody has an opinion on a subject, represented by a vector of probabilities $p(0)\; =\; (p\_1(0),\; \backslash dots,\; p\_n(0)\; )$. Agents obtain no new information based on which they can update their opinions but they communicate with other agents. Links between agents (who knows whom) and the weight they put on each other's opinions is represented by a trust matrix $T$ where $T\_$ is the weight that agent $i$ puts on agent $j$'s opinion. The trust matrix is thus in a one-to-one relationship with a weighted, directed graph where there is an edge between $i$ and $j$ if and only if $T\_\; >\; 0$. The trust matrix is stochastic, its rows consists of nonnegative real numbers, with each row summing to 1. Formally, the beliefs are updated in each period as :$$ p(t) = T p(t-1) so the $t$ th period opinions are related to the initial opinions by :$$ p(t) = T^t p(0) 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「DeGroot learning」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.