distributional (version 0.2.1)

dist_bernoulli: The Bernoulli distribution

Description

stable

Usage

dist_bernoulli(prob)

Arguments

prob

The probability of success on each trial, prob can be any value in [0, 1].

Details

Bernoulli distributions are used to represent events like coin flips when there is single trial that is either successful or unsuccessful. The Bernoulli distribution is a special case of the Binomial() distribution with n = 1.

We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.

In the following, let \(X\) be a Bernoulli random variable with parameter p = \(p\). Some textbooks also define \(q = 1 - p\), or use \(\pi\) instead of \(p\).

The Bernoulli probability distribution is widely used to model binary variables, such as 'failure' and 'success'. The most typical example is the flip of a coin, when \(p\) is thought as the probability of flipping a head, and \(q = 1 - p\) is the probability of flipping a tail.

Support: \(\{0, 1\}\)

Mean: \(p\)

Variance: \(p \cdot (1 - p) = p \cdot q\)

Probability mass function (p.m.f):

$$ P(X = x) = p^x (1 - p)^{1-x} = p^x q^{1-x} $$

Cumulative distribution function (c.d.f):

$$ P(X \le x) = \left \{ \begin{array}{ll} 0 & x < 0 \\ 1 - p & 0 \leq x < 1 \\ 1 & x \geq 1 \end{array} \right. $$

Moment generating function (m.g.f):

$$ E(e^{tX}) = (1 - p) + p e^t $$

Examples

# NOT RUN {
dist <- dist_bernoulli(prob = c(0.05, 0.5, 0.3, 0.9, 0.1))

dist
mean(dist)
variance(dist)
skewness(dist)
kurtosis(dist)

generate(dist, 10)

density(dist, 2)
density(dist, 2, log = TRUE)

cdf(dist, 4)

quantile(dist, 0.7)

# }