BinomialRandomVariable object

Defines a Binomial random variable. It uses in the backend all functions that defines a Binomial random variable. As the other definitions in distributions this is an R6 object. Tha parametrization used is from a $$X \sim \mathsf{Binomial}(n, \theta)\,,$$ where the parameters \(n\) and \(\theta\) are the number of independent trials and the rate (probability) of success.

Note

All random variables in distributions are defined as R6 objects. The fields (referenced below) are needed for objects of R6::R6Class. In our context these are what defines which specific instance of a random variable is used. That is, the public fields are the parameters of the random variable \(\theta \in \mathbb{R}^p\), for some \(p\).

See also

Other discrete: PoissonRandomVariable

Super class

distributions::DiscreteRandomVariable -> Binomial

Public fields

n

(integer)
the number of independent trials, \(n\).

theta

(double, constrained)
the probability of success of an independent trial, \(\theta \in [0,1]\).

Methods

Public methods

• BinomialRandomVariable$new()

• BinomialRandomVariable$sample()

• BinomialRandomVariable$density()

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Method new()

Generates a Binomial DiscreteRandomVariable object with specified number of trials (n) and probability of success (theta).

Usage

BinomialRandomVariable$new(n = 10, theta = 0.5)

Arguments

n

(integer) the center of the distribution.

theta

(double, constrained) the dispersion around the center of the distribution.

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Method sample()

Generates random variables using the stats::rbinom() function.

Usage

BinomialRandomVariable$sample(nsamples = 1, nreps = 1)

Arguments

nsamples

(integer) the number of random numbers being generated that behaves like the random variables.

nreps

(integer) the number of sequences of size nsamples to be generated.

Returns

(array) of random numbers from the distribution.

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Method density()

Evaluates the density function using the stats::dbinom() function.

Usage

BinomialRandomVariable$density(x, log = TRUE)

Arguments

x

(array) to evaluate the density function.

log

(Boolean) indicates if we are evaluating the log-density.

Returns

(array) of evaluations at x.