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A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), [1] is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value, called ...
AutoNumber is a type of data used in Microsoft Access tables to generate an automatically incremented numeric counter. It may be used to create an identity column which uniquely identifies each record of a table.
To generate a sequence of n-digit pseudorandom numbers, an n-digit starting value is created and squared, producing a 2n-digit number. If the result has fewer than 2n digits, leading zeroes are added to compensate. The middle n digits of the result would be the next number in the sequence and returned as the result. This process is then ...
A sequence number is a consecutive number in a sequence of numbers, usually of real integers (natural numbers).Sequence numbers have many practical applications. They can be used, among other things, as part of serial numbers on manufactured parts, in case management, [1] or in databases as a surrogate key for registering and identifying unique entries in a table [2] [3] (in which case it is ...
A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator.. A pseudorandom number generator's number sequence is completely determined by the seed: thus, if a pseudorandom number generator is later reinitialized with the same seed, it will produce the same sequence of numbers.
Prefix sums are trivial to compute in sequential models of computation, by using the formula y i = y i − 1 + x i to compute each output value in sequence order. However, despite their ease of computation, prefix sums are a useful primitive in certain algorithms such as counting sort, [1] [2] and they form the basis of the scan higher-order function in functional programming languages.
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For the most part, statistical analysis has, in practice, been much more concerned with finding regularities in data as opposed to testing for randomness. Many "random number generators" in use today are defined by algorithms, and so are actually pseudo-random number generators. The sequences they produce are called pseudo-random sequences.
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