BetaRandomVariable object

Defines a Beta random variable. It uses in the backend all functions that defines a Beta random variable. As the other definitions in distributions this is an R6 object. Tha parametrization used is from a $$X \sim \mathsf{Beta}(\alpha, \beta)\,,$$ where the parameters \(\alpha\) and \(\beta\) are the parameters defining a Beta random variable.

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 continuous: GaussianRandomVariable, UniformRandomVariable

Super class

distributions::ContinuousRandomVariable -> Beta

Public fields

alpha

(double, positive)
the first parameter of the distribution, \(\alpha > 0\).

beta

(double, positive)
the second parameter of the distribution, \(\beta > 0\).

Methods

Public methods

• BetaRandomVariable$new()

• BetaRandomVariable$sample()

• BetaRandomVariable$density()

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Method new()

Generates a Beta ContinuousRandomVariable object with specified parameters alpha (alpha) and beta (beta).

Usage

BetaRandomVariable$new(alpha = 1, beta = 1)

Arguments

alpha

(double, positive) the first parameter.

beta

(double, positive) the second parameter.

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Method sample()

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

Usage

BetaRandomVariable$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::dbeta() function.

Usage

BetaRandomVariable$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.