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A discrete probability distribution is the probability distribution of a random variable that can take on only a countable number of values [15] (almost surely) [16] which means that the probability of any event can be expressed as a (finite or countably infinite) sum: = (=), where is a countable set with () =.
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] or (0, 1) in terms of two positive parameters, denoted by alpha (α) and beta (β), that appear as exponents of the variable and its complement to 1, respectively, and control the shape of the distribution.
The Gompertz distribution is a flexible distribution that can be skewed to the right and to the left. Its hazard function = is a convex function of (;,).The model can be fitted into the innovation-imitation paradigm with = as the coefficient of innovation and as the coefficient of imitation.
In probability theory and statistics, the Bernoulli distribution, named after Swiss mathematician Jacob Bernoulli, [1] is the discrete probability distribution of a random variable which takes the value 1 with probability and the value 0 with probability =.
Pólya urn model; Probabilistic automaton; Probabilistic classification; Probabilistic context-free grammar; Probabilistic logic programming; Probabilistic programming; Probabilistic relevance model; Probabilistic relevance model (BM25) Probabilistic voting model
Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations , probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms .
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