翻訳と辞書 Words near each other ・ Uniformology ・ Uniforms and insignia of the Kriegsmarine ・ Uniforms and insignia of the Schutzstaffel ・ Uniforms and insignia of the Sturmabteilung ・ Uniforms of Iraqi Armed Forces ・ Uniforms of La Grande Armée ・ Uniform crake ・ Uniform Crime Reporting Handbook ・ Uniform Crime Reports ・ Uniform Customs and Practice for Documentary Credits ・ Uniform data access ・ Uniform Debt-Management Services Act ・ Uniform Determination of Death Act ・ Uniform distribution ・ Uniform distribution (continuous) ・ Uniform distribution (discrete) ・ Uniform Distribution and Accreditation Centre ・ Uniform Domain-Name Dispute-Resolution Policy ・ Uniform Driver Interface ・ Uniform Electronic Legal Material Act ・ Uniform Electronic Transactions Act ・ Uniform Environmental Covenants Act ・ Uniform Evaluation ・ Uniform fetishism ・ Uniform field theory ・ Uniform finch ・ Uniform Firearms Act ・ Uniform Fourpenny Post ・ Uniform Function Call Syntax ・ Uniform Gifts to Minors Act Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Uniform distribution (discrete) ： ウィキペディア英語版 Uniform distribution (discrete) \,| char =$\backslash frac\}$| }} In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution whereby a finite number of values are equally likely to be observed; every one of ''n'' values has equal probability ''1/n''. Another way of saying "discrete uniform distribution" would be "a known, finite number of outcomes equally likely to happen". A simple example of the discrete uniform distribution is throwing a fair die. The possible values are 1, 2, 3, 4, 5, 6, and each time the die is thrown the probability of a given score is 1/6. If two dice are thrown and their values added, the resulting distribution is no longer uniform since not all sums have equal probability. The discrete uniform distribution itself is inherently non-parametric. It is convenient, however, to represent its values generally by an integer interval ''()'', so that ''a,b'' become the main parameters of the distribution (often one simply considers the interval ''()'' with the single parameter ''n''). With these conventions, the cumulative distribution function (CDF) of the discrete uniform distribution can be expressed, for any ''k'' ∈ ''()'', as :$F(k;a,b)=\backslash frac$ ==Estimation of maximum== (詳細はGerman tank problem, following the application of maximum estimation to estimates of German tank production during World War II. The UMVU estimator for the maximum is given by :$\backslash hat=\backslash frac\; m\; -\; 1\; =\; m\; +\; \backslash frac\; -\; 1$ where ''m'' is the sample maximum and ''k'' is the sample size, sampling without replacement. This can be seen as a very simple case of maximum spacing estimation. The formula may be understood intuitively as: :"The sample maximum plus the average gap between observations in the sample", the gap being added to compensate for the negative bias of the sample maximum as an estimator for the population maximum.〔The sample maximum is never more than the population maximum, but can be less, hence it is a biased estimator: it will tend to ''underestimate'' the population maximum.〕 This has a variance of〔 :$\backslash frac\backslash frac\; \backslash approx\; \backslash frac\; \backslash text\; k\; \backslash ll\; N$ so a standard deviation of approximately $\backslash tfrac\; N\; k$, the (population) average size of a gap between samples; compare $\backslash tfrac$ above. The sample maximum is the maximum likelihood estimator for the population maximum, but, as discussed above, it is biased. If samples are not numbered but are recognizable or markable, one can instead estimate population size via the capture-recapture method. 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「Uniform distribution (discrete)」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.