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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 Multinomial probit ： ウィキペディア英語版 Multinomial probit In statistics and econometrics, the multinomial probit model is a generalization of the probit model used when there are several possible categories that the dependent variable can fall into. As such, it is an alternative to the multinomial logit model as one method of multiclass classification. It is not to be confused with the ''multivariate'' probit model, which is used to model correlated binary outcomes for more than one independent variable. ==General specification== It is assumed that we have a series of observations ''Y''''i'', for ''i'' = 1...''n'', of the outcomes of multi-way choices from a categorical distribution of size ''m'' (there are ''m'' possible choices). Along with each observation ''Y''''i'' is a set of ''k'' observed values ''x''''1,i'', ..., ''x''''k,i'' of explanatory variables (also known as independent variables, predictor variables, features, etc.). Some examples: *The observed outcomes might be "has disease A, has disease B, has disease C, has none of the diseases" for a set of rare diseases with similar symptoms, and the explanatory variables might be characteristics of the patients thought to be pertinent (sex, race, age, blood pressure, body-mass index, presence or absence of various symptoms, etc.). *The observed outcomes are the votes of people for a given party or candidate in a multi-way election, and the explanatory variables are the demographic characteristics of each person (e.g. sex, race, age, income, etc.). The multinomial probit model is a statistical model that can be used to predict the likely outcome of an unobserved multi-way trial given the associated explanatory variables. In the process, the model attempts to explain the relative effect of differing explanatory variables on the different outcomes. Formally, the outcomes ''Y''''i'' are described as being categorically-distributed data, where each outcome value ''h'' for observation ''i'' occurs with an unobserved probability ''p''''i,h'' that is specific to the observation ''i'' at hand because it is determined by the values of the explanatory variables associated with that observation. That is: :$Y\_i|x\_,\backslash ldots,x\_\; \backslash \; \backslash sim\; \backslash operatorname(p\_i,\backslash ldots,p\_m),\backslash texti\; =\; 1,\; \backslash dots\; ,\; n$ or equivalently :$\backslash Pr()\; =\; p\_,\backslash texti\; =\; 1,\; \backslash dots\; ,\; n,$ for each of ''m'' possible values of ''h''. 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「Multinomial probit」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.