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The principle of transformation groups is a rule for assigning ''epistemic'' probabilities in a statistical inference problem. It was first suggested by Edwin T Jaynes 〔http://bayes.wustl.edu/etj/articles/prior.pdf〕 and can be seen as a generalisation of the principle of indifference. This can be seen as a method to create ''objective ignorance probabilities'' in the sense that two people who apply the principle and are confronted with the same information will assign the same probabilities. == Motivation and description of the method == The method is motivated by the following normative principle, or desideratum: ''In two problems where we have the same prior information we should assign the same prior probabilities'' The method then comes about from "transforming" a given problem into an equivalent one. This method has close connections with group theory, and to a large extent is about finding symmetry in a given problem, and then exploiting this symmetry to assign prior probabilities. In problems with discrete variables (e.g. dice, cards, categorical data) the principle reduces to the principle of indifference, as the "symmetry" in the discrete case is a permutation of the labels, that is the permutation group is the relevant transformation group for this problem. In problems with continuous variables, this method generally reduces to solving a differential equation. Given that differential equations do not always lead to unique solutions, this method cannot be guaranteed to produce a unique solution. However, in a large class of the most common types of parameters it does lead to unique solutions (see the examples below) 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「Principle of transformation groups」の詳細全文を読む スポンサード リンク
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