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  2. Bernoulli distribution - Wikipedia

    en.wikipedia.org/wiki/Bernoulli_distribution

    The categorical distribution is the generalization of the Bernoulli distribution for variables with any constant number of discrete values. The Beta distribution is the conjugate prior of the Bernoulli distribution. [5] The geometric distribution models the number of independent and identical Bernoulli trials needed to get one success.

  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. Poisson binomial distribution - Wikipedia

    en.wikipedia.org/wiki/Poisson_binomial_distribution

    An R package poibin was provided along with the paper, [13] which is available for the computing of the cdf, pmf, quantile function, and random number generation of the Poisson binomial distribution. For computing the PMF, a DFT algorithm or a recursive algorithm can be specified to compute the exact PMF, and approximation methods using the ...

  5. 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.

  6. Relationships among probability distributions - Wikipedia

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

    2) random variable. The sum of N chi-squared (1) random variables has a chi-squared distribution with N degrees of freedom. Other distributions are not closed under convolution, but their sum has a known distribution: The sum of n Bernoulli (p) random variables is a binomial (n, p) random variable.

  7. Completeness (statistics) - Wikipedia

    en.wikipedia.org/wiki/Completeness_(statistics)

    The Bernoulli model admits a complete statistic. [3] Let X be a random sample of size n such that each X i has the same Bernoulli distribution with parameter p. Let T be the number of 1s observed in the sample, i.e. = =. T is a statistic of X which has a binomial distribution with parameters (n,p).

  8. Bernoulli trial - Wikipedia

    en.wikipedia.org/wiki/Bernoulli_trial

    Random variables describing Bernoulli trials are often encoded using the convention that 1 = "success", 0 = "failure". Closely related to a Bernoulli trial is a binomial experiment, which consists of a fixed number n {\displaystyle n} of statistically independent Bernoulli trials, each with a probability of success p {\displaystyle p} , and ...

  9. Bernoulli process - Wikipedia

    en.wikipedia.org/wiki/Bernoulli_process

    The term Bernoulli sequence is often used informally to refer to a realization of a Bernoulli process. However, the term has an entirely different formal definition as given below. Suppose a Bernoulli process formally defined as a single random variable (see preceding section). For every infinite sequence x of coin flips, there is a sequence of ...