List probability distribution function in random process
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Many probability distributions that are important in theory or applications have been given specific names.
Discrete distributions Edit
Binomial distribution
Degenerate distribution
With finite support Edit
The Bernoulli distribution, which takes value 1 with probability p and value 0 with probability q = 1 − p.
The Rademacher distribution, which takes value 1 with probability 1/2 and value −1 with probability 1/2.
The binomial distribution, which describes the number of successes in a series of independent Yes/No experiments all with the same probability of success.
The beta-binomial distribution, which describes the number of successes in a series of independent Yes/No experiments with heterogeneity in the success probability.
The degenerate distribution at x0, where X is certain to take the value x0. This does not look random, but it satisfies the definition of random variable. This is useful because it puts deterministic variables and random variables in the same formalism.
The discrete uniform distribution, where all elements of a finite set are equally likely. This is the theoretical distribution model for a balanced coin, an unbiased die, a casino roulette, or the first card of a well-shuffled deck.
The hypergeometric distribution, which describes the number of successes in the first m of a series of n consecutive Yes/No experiments, if the total number of successes is known. This distribution arises when there is no replacement.
The Poisson binomial distribution, which describes the number of successes in a series of independent Yes/No experiments with different success probabilities.
Fisher's noncentral hypergeometric distribution
Wallenius' noncentral hypergeometric distribution
Benford's law, which describes the frequency of the first digit of many naturally occurring data.
Discrete distributions Edit
Binomial distribution
Degenerate distribution
With finite support Edit
The Bernoulli distribution, which takes value 1 with probability p and value 0 with probability q = 1 − p.
The Rademacher distribution, which takes value 1 with probability 1/2 and value −1 with probability 1/2.
The binomial distribution, which describes the number of successes in a series of independent Yes/No experiments all with the same probability of success.
The beta-binomial distribution, which describes the number of successes in a series of independent Yes/No experiments with heterogeneity in the success probability.
The degenerate distribution at x0, where X is certain to take the value x0. This does not look random, but it satisfies the definition of random variable. This is useful because it puts deterministic variables and random variables in the same formalism.
The discrete uniform distribution, where all elements of a finite set are equally likely. This is the theoretical distribution model for a balanced coin, an unbiased die, a casino roulette, or the first card of a well-shuffled deck.
The hypergeometric distribution, which describes the number of successes in the first m of a series of n consecutive Yes/No experiments, if the total number of successes is known. This distribution arises when there is no replacement.
The Poisson binomial distribution, which describes the number of successes in a series of independent Yes/No experiments with different success probabilities.
Fisher's noncentral hypergeometric distribution
Wallenius' noncentral hypergeometric distribution
Benford's law, which describes the frequency of the first digit of many naturally occurring data.
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