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  2. Continuity correction - Wikipedia

    en.wikipedia.org/wiki/Continuity_correction

    A particular example of this is the binomial test, involving the binomial distribution, as in checking whether a coin is fair. Where extreme accuracy is not necessary, computer calculations for some ranges of parameters may still rely on using continuity corrections to improve accuracy while retaining simplicity.

  3. Binomial distribution - Wikipedia

    en.wikipedia.org/wiki/Binomial_distribution

    A Binomial distributed random variable X ~ B(n, p) can be considered as the sum of n Bernoulli distributed random variables. So the sum of two Binomial distributed random variables X ~ B(n, p) and Y ~ B(m, p) is equivalent to the sum of n + m Bernoulli distributed random variables, which means Z = X + Y ~ B(n + m, p). This can also be proven ...

  4. Convolution of probability distributions - Wikipedia

    en.wikipedia.org/wiki/Convolution_of_probability...

    The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density functions respectively.

  5. List of convolutions of probability distributions - Wikipedia

    en.wikipedia.org/wiki/List_of_convolutions_of...

    In probability theory, the probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density ...

  6. Relationships among probability distributions - Wikipedia

    en.wikipedia.org/wiki/Relationships_among...

    If X is a beta (α, β) random variable then (1 − X) is a beta (β, α) random variable. If X is a binomial (n, p) random variable then (n − X) is a binomial (n, 1 − p) random variable. If X has cumulative distribution function F X, then the inverse of the cumulative distribution F X (X) is a standard uniform (0,1) random variable

  7. List of probability distributions - Wikipedia

    en.wikipedia.org/wiki/List_of_probability...

    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 ...

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  9. Stochastic simulation - Wikipedia

    en.wikipedia.org/wiki/Stochastic_simulation

    A binomial distributed random variable Y with parameters n and p is obtained as the sum of n independent and identically Bernoulli-distributed random variables X 1, X 2, ..., X n [4] Example: A coin is tossed three times. Find the probability of getting exactly two heads. This problem can be solved by looking at the sample space.