Methods | Statistics | Clinical | Educational | Industrial | Professional items | World psychology |
The many applications of randomness have led to many different methods for generating random data. These methods may vary as to how unpredictable or statistically random they are, and how quickly they can generate random numbers.
Before the advent of computational random number generators, generating large amount of sufficiently random numbers (important in statistics) required a lot of work. Results would sometimes be collected and distributed as random number tables.
The earliest methods for generating random numbers - dice, coin flipping, roulette wheels are still used today, mainly in games and gambling as they tend to be too slow for applications in statistics and cryptography.
Some physical phenomena, such as thermal noise in zener diodes appear to be truly random and can be used as the basis for hardware random number generators. However, many mechanical phenomena feature asymmetries and systematic biases that make their outcomes not truly random. The many successful attempts to exploit such phenomena by gamblers, especially in roulette and blackjack are testimony to these effects.
Completely randomized design falls within the category of true random number generation. The generation of true random numbers outside the computer environment is based on the theory of entropy. Sources of entropy include nuclear decay and atmospheric conditions. HotBits uses radioactive decay, while Random.org uses radio noise to generate randomness.
Pseudo-random number generators (PRNGs) are algorithms that can automatically create long runs (for example, millions of numbers long) with good random properties but eventually the sequence repeats exactly (or the memory usage grows without bound). One of the most common PRNG is the linear congruential generator which uses the recurrence
to generate numbers. The maximum number of numbers the formula can produce is the modulus, m.
A pen-and-paper method proven to generate truly random numbers exploits the fact that the decimal expansion of the reciprocal 1/q will produce a stream of random numbers of length q - 1 if q is such that q = 2S + 1 where S is a Sophie Germain prime, and both S and 2S + 1 are of the form 3, 9 or 11 mod 20. Thus “suitable” prime numbers q are 7, 23, 47, 59, 167, 179, etc (corresponding to S = 3, 11, 23, 29, 83, 89, etc.). The result is a stream of length q-1 digits (including leading zeros). So, for example, using q = 23 generates the random digits 0,4,3,4,7,8,2,6.....3,9,1,3
Random number generators are very useful in developing Monte Carlo simulations as debugging is facilitated by the ability to run the same sequence of random numbers again by starting from the same 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 villain might move in a computer game. Weaker forms of randomness are also closely associated with 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 to be random; a true random system would have no restriction on the same item appearing two or three times in succession.
|This page uses Creative Commons Licensed content from Wikipedia (view authors).|