gleam_stats - v1.0.0

gleam_stats/distributions/normal

Functions related to continuous normal random variables.

Functions

normal_cdf

</> 
pub fn normal_cdf(x: Float, mu: Float, sigma: Float) -> Result(
  Float,
  String,
)

Evaluate, at a certain point, the cumulative distribution function (cdf) of a continuous normal random variable with mean ‘mu’ and standard deviation ‘sigma’.

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

 pub fn example() {
   let mean: Float = 0.
   let sigma: Float = 1.
   // For illustrational purposes, evaluate the cdf at the 
   // point -100.0
   normal.normal_cdf(-100.0, mu, sigma) |> should.equal(Ok(0.0))
 }

normal_mean

</> 
pub fn normal_mean(mu: Float, sigma: Float) -> Result(
  Float,
  String,
)

Analytically compute the mean of a continuous normal random variable
with given mean ‘mu’ and standard deviation ‘sigma’.

normal_pdf

</> 
pub fn normal_pdf(x: Float, mu: Float, sigma: Float) -> Result(
  Float,
  String,
)

Evaluate the probability density function (pdf) of a continuous normal random variable with given mean ‘mu’ and standard deviation ‘sigma’.

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

 pub fn example() {
   let mean: Float = 0.
   let sigma: Float = 1.
   // For illustrational purposes, evaluate the pdf at the 
   // point -100.0
   normal.normal_pdf(-100.0, mu, sigma) |> should.equal(Ok(0.0))
 }

normal_random

</> 
pub fn normal_random(stream: Iterator(Int), mu: Float, sigma: Float, m: Int) -> Result(
  #(List(Float), Iterator(Int)),
  String,
)

Generate ‘m’ random numbers from a continuous normal distribution with a given mean ‘mu’ and standard deviation ‘sigma’.

The random numbers are generated using Box–Muller transform.

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

 pub fn example() {
   let seed: Int = 5
   let seq: Int = 1
   let mean: Float = 0.
   let std: Float = 1.
   assert Ok(out) =
     generators.seed_pcg32(seed, seq)
     |> normal.normal_random(mean, std, 5_000)
   let rands: List(Float) = pair.first(out)
   let stream: Iterator(Int) = pair.second(out)
 }

normal_variance

</> 
pub fn normal_variance(mu: Float, sigma: Float) -> Result(
  Float,
  String,
)

Analytically compute the variance of a continuous normal random variable
with given mean ‘mu’ and standard deviation ‘sigma’.

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