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A random number generator (RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that can not be reasonably predicted better than by a random chance. Various applications of randomness have led to the development of several different methods for generating random data, of which some existed since ancient times, including dice, coin flipping, the shuffling of playing cards, the use of yarrow stalks (by divination) in the I Ching, and many other techniques. Because of the mechanical nature of these techniques, generating large numbers of sufficiently random numbers (important in statistics) required a lot of work and/or time. Thus, results would sometimes be collected and distributed as random number tables. Nowadays, after the advent of computational random number generators, a growing number of government-run lotteries, and lottery games, are using RNGs instead of more traditional drawing methods. RNGs are also used to determine the odds of modern slot machines.〔(【引用サイトリンク】 Introduction to Slot Machines )〕 Several computational methods for random number generation exist. Many fall short of the goal of true randomness, although they may meet, with varying success, some of the statistical tests for randomness intended to measure how unpredictable their results are (that is, to what degree their patterns are discernible). However, carefully designed cryptographically secure computationally based methods of generating random numbers do exist, such as those based on the Yarrow algorithm and the Fortuna (PRNG) and others. == Practical applications and uses == (詳細はgambling, statistical sampling, computer simulation, cryptography, completely randomized design, and other areas where producing an unpredictable result is desirable. Generally, in applications having unpredictability as the paramount, such as in security applications, hardware generators are generally preferred over pseudo-random algorithms, where feasible. Random number generators are very useful in developing Monte Carlo-method simulations, as debugging is facilitated by the ability to run the same sequence of random numbers again by starting from the same ''random seed''. They are also used in cryptography – so long as the ''seed'' is secret. Sender and receiver can generate the same set of numbers automatically to use as keys. The generation of pseudo-random numbers is an important and common task in computer programming. While cryptography and certain numerical algorithms require a very high degree of ''apparent'' randomness, many other operations only need a modest amount of unpredictability. Some simple examples might be presenting a user with a "Random Quote of the Day", or determining which way a computer-controlled adversary might move in a computer game. Weaker forms of ''randomness'' are used in hash algorithms and in creating amortized searching and sorting algorithms. Some applications which appear at first sight to be suitable for randomization are in fact not quite so simple. For instance, a system that "randomly" selects music tracks for a background music system must only ''appear'' random, and may even have ways to control the selection of music: a true random system would have no restriction on the same item appearing two or three times in succession. 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「Random number generation」の詳細全文を読む スポンサード リンク
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