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In probability theory, the sample space (also called sample description space, [1] possibility space, [2] or outcome space [3]) of an experiment or random trial is the set of all possible outcomes or results of that experiment. [4] A sample space is usually denoted using set notation, and the possible ordered outcomes, or sample points, [5] are ...
In theoretical computer science, a small-bias sample space (also known as -biased sample space, -biased generator, or small-bias probability space) is a probability distribution that fools parity functions . In other words, no parity function can distinguish between a small-bias sample space and the uniform distribution with high probability ...
A random variable is a measurable function from a sample space as a set of possible outcomes to a measurable space . The technical axiomatic definition requires the sample space to be a sample space of a probability triple (see the measure-theoretic definition ). A random variable is often denoted by capital Roman letters such as .
A probability space is a mathematical triplet that presents a model for a particular class of real-world situations. As with other models, its author ultimately defines which elements , , and will contain. The sample space is the set of all possible outcomes. An outcome is the result of a single execution of the model.
Markov processes can also be used to generate superficially real-looking text given a sample document. Markov processes are used in a variety of recreational " parody generator " software (see dissociated press , Jeff Harrison, [111] Mark V. Shaney , [112] [113] and Academias Neutronium).
Latin hypercube sampling ( LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. The sampling method is often used to construct computer experiments or for Monte Carlo integration . LHS was described by Michael McKay of Los Alamos National Laboratory in 1979. [1]
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 ...
Pseudorandom generator. In theoretical computer science and cryptography, a pseudorandom generator (PRG) for a class of statistical tests is a deterministic procedure that maps a random seed to a longer pseudorandom string such that no statistical test in the class can distinguish between the output of the generator and the uniform distribution.