probabilityMassFunction
13
triples
GPTKB property
Random triples
| Subject | Object |
|---|---|
| gptkb:Bernoulli_random_variable | P(X=1)=p, P(X=0)=1-p |
| gptkb:Categorical_distribution | P(X=i) = p_i |
| gptkb:Poisson_statistics | P(k;λ) = (λ^k * e^(-λ)) / k! |
| gptkb:Multinomial_distribution | P(X1=x1,...,Xk=xk) = n!/(x1!...xk!) * p1^x1 * ... * pk^xk |
| gptkb:hypergeometric_distribution | P(X = k) = [C(K, k) * C(N-K, n-k)] / C(N, n) |
| 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:Beta-binomial_distribution | P(X = k) = (n choose k) * B(k+α, n−k+β) / B(α, β) |
| gptkb:Poisson_distribution | P(k; λ) = (λ^k * e^{-λ}) / k! |
| gptkb:Conway–Maxwell–Poisson_distribution | P(X=k) = (lambda^k) / (k!)^nu * 1/Z(lambda, nu) |
| gptkb:Yule_distribution | f(k; c) = c b(k-1, c+1) / b(k+1, 2+c) for k |
| gptkb:Conway-Maxwell-Poisson_distribution | P(X=k) = (lambda^k)/(k!)^nu * 1/Z(lambda, nu) |
| gptkb:COM-Poisson_distribution | P(X=k) = (lambda^k)/(k!)^nu * 1/Z(lambda, nu) |