13_PD_poisson: Poisson Distributions

In bivariate: Bivariate Probability Distributions

Description

Bivariate Poisson distributions.

Usage

pbvpmf (lambda.1, lambda.2, lambda.3)
pbvcdf (lambda.1, lambda.2, lambda.3)

pbvpmf.2 (mean.X, mean.Y, cov)
pbvcdf.2 (mean.X, mean.Y, cov)

Arguments

lambda.1, lambda.2, lambda.3	Positive numeric values, giving the first, second and third lambda parameters.
mean.X, mean.Y	Suitable numeric values, giving the mean of X and Y. Note that their means equal their variances.
cov	Suitable numeric value, giving the covariance of X and Y.

Value

Self-referencing S4-based function objects.

Refer to Function Objects.

References

Refer to the vignette for an overview, references, theoretical background and better examples.

See Also

Uniform
For uniform distributions.

Binomial and Categorical
For other probability distributions of discrete random variables.

Normal, Bimodal, Dirichlet and Nonparametric
For other probability distributions of continuous random variables.

Main Plotting Functions

Density Matrices

Examples

f <- pbvpmf (10, 10, 0)

plot (f)
f (5, 5)

bivariate documentation built on April 11, 2021, 9:06 a.m.