probabilityMassFunction

13 triples
GPTKB property

Random triples
Subject Object
gptkb:hypergeometric_distribution P(X = k) = [C(K, k) * C(N-K, n-k)] / C(N, n)
gptkb:Categorical_distribution P(X=i) = p_i
gptkb:Conway-Maxwell-Poisson_distribution P(X=k) = (lambda^k)/(k!)^nu * 1/Z(lambda, nu)
gptkb:Beta-binomial_distribution P(X = k) = (n choose k) * B(k+α, n−k+β) / B(α, β)
gptkb:Conway–Maxwell–Poisson_distribution P(X=k) = (lambda^k) / (k!)^nu * 1/Z(lambda, nu)
gptkb:logseries_distribution P(X = x) = -theta^x / (x * log(1 - theta)), 0 < theta < 1
gptkb:Fisher's_logseries P(n) = (α x^n) / n
gptkb:COM-Poisson_distribution P(X=k) = (lambda^k)/(k!)^nu * 1/Z(lambda, nu)
gptkb:Multinomial_distribution P(X1=x1,...,Xk=xk) = n!/(x1!...xk!) * p1^x1 * ... * pk^xk
gptkb:Bernoulli_random_variable P(X=1)=p, P(X=0)=1-p
gptkb:Poisson_statistics P(k;λ) = (λ^k * e^(-λ)) / k!
gptkb:Yule_distribution f(k; c) = c b(k-1, c+1) / b(k+1, 2+c) for k
gptkb:Poisson_distribution P(k; λ) = (λ^k * e^{-λ}) / k!