gleam_stats - v1.0.0

gleam_stats/distributions/bernoulli

Functions related to discrete bernoulli random variables.

Functions

bernoulli_cdf

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pub fn bernoulli_cdf(x: Int, p: Float) -> Result(Float, String)

Evaluate, at a certain point, the cumulative distribution function (cdf) of a discrete bernoulli distribution with parameter ‘p’ in the interval [0, 1] (the success probability of a trial).

Example: 
 import gleam_stats/distributions/bernoulli

 pub fn example() {
   let p: Float = 0.5
   // For illustrational purposes, evaluate the cdf at the 
   // point -100.0
   bernoulli.bernoulli_cdf(-100.0, p) |> should.equal(0.0)
 }

bernoulli_mean

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pub fn bernoulli_mean(p: Float) -> Result(Float, String)

Analytically compute the mean of a discrete bernoulli distribution with parameter ‘p’ in the interval [0, 1] (the success probability of a trial).

bernoulli_pmf

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pub fn bernoulli_pmf(x: Int, p: Float) -> Result(Float, String)

Evaluate the probability mass function (pmf) of a discrete bernoulli distribution with parameter ‘p’ in the interval [0, 1] (the success probability of a trial).

Example: 
 import gleam_stats/distributions/bernoulli

 pub fn example() {
   let p: Float = 0.5
   // For illustrational purposes, evaluate the pmf at the 
   // point -100.0
   bernoulli.bernoulli_pmf(-100.0, p) |> should.equal(0.0)
 }

bernoulli_random

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pub fn bernoulli_random(stream: Iterator(Int), p: Float, m: Int) -> Result(
  #(List(Int), Iterator(Int)),
  String,
)

Generate ‘m’ random numbers from a discrete bernoulli distribution with parameter ‘p’ in the interval [0, 1] (the success probability of a trial).

The random numbers are generated using the inverse transform method.

Example: 
 import gleam/iterator.{Iterator}
 import gleam_stats/generator
 import gleam_stats/distributions/bernoulli

 pub fn example() {
   let seed: Int = 5
   let seq: Int = 1
   let p: Float = 0.5
   assert Ok(out) =
     generators.seed_pcg32(seed)
     |> bernoulli.bernoulli_random(p, 5_000)
   let rands: List(Float) = pair.first(out)
   let stream: Iterator(Int) = pair.second(out)
 }

bernoulli_variance

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pub fn bernoulli_variance(p: Float) -> Result(Float, String)

Analytically compute the variance of a discrete bernoulli distribution with parameter ‘p’ in the interval [0, 1] (the success probability of a trial).

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