gleam_stats - v1.0.0

gleam_stats/distributions/exponential

Functions related to continuous exponential random variables.

Functions

exponential_cdf

</> 
pub fn exponential_cdf(x: Float, lambda: Float) -> Result(
  Float,
  String,
)

Evaluate, at a certain point, the cumulative distribution function (cdf) of a continuous exponential random variable with given rate parameter ‘lambda’ > 0.

Example: 
 import gleam_stats/distributions/exponential
 import gleeunit/should

 pub fn example() {
   let lambda: Float = 1.
   // For illustrational purposes, evaluate the cdf at the 
   // point -100.0
   exponential.exponential_cdf(-100.0, lambda) |> should.equal(Ok(0.0))
 }

exponential_mean

</> 
pub fn exponential_mean(lambda: Float) -> Result(Float, String)

Analytically compute the mean of a continuous exponential random variable
with given rate parameter ‘lambda’ > 0.

exponential_pdf

</> 
pub fn exponential_pdf(x: Float, lambda: Float) -> Result(
  Float,
  String,
)

Evaluate the probability density function (pdf) of a continuous exponential random variable with given rate parameter ‘lambda’ > 0.

Example: 
 import gleam_stats/distributions/exponential
 import gleeunit/should

 pub fn example() {
   let lambda: Float = 1.
   // For illustrational purposes, evaluate the pdf at the 
   // point -100.0
   exponential.exponential_pdf(-100.0, lambda) |> should.equal(Ok(0.0))
 }

exponential_random

</> 
pub fn exponential_random(stream: Iterator(Int), lambda: Float, m: Int) -> Result(
  #(List(Float), Iterator(Int)),
  String,
)

Generate ‘m’ random numbers from a continuous exponential distribution with given rate parameter ‘lambda’ > 0.

The random numbers are generated using the inverse transform method.

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

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

exponential_variance

</> 
pub fn exponential_variance(lambda: Float) -> Result(
  Float,
  String,
)

Analytically compute the variance of a continuous exponential random variable
with given rate parameter ‘lambda’ > 0.

✨