pub struct LogNormal<F>{ /* private fields */ }Expand description
The log-normal distribution ln N(μ, σ²).
This is the distribution of the random variable X = exp(Y) where Y is
normally distributed with mean μ and variance σ². In other words, if
X is log-normal distributed, then ln(X) is N(μ, σ²) distributed.
§Plot
The following diagram shows the log-normal distribution with various values
of μ and σ.
§Example
use rand_distr::{LogNormal, Distribution};
// mean 2, standard deviation 3
let log_normal = LogNormal::new(2.0, 3.0).unwrap();
let v = log_normal.sample(&mut rand::rng());
println!("{} is from an ln N(2, 9) distribution", v)Implementations§
Source§impl<F> LogNormal<F>
impl<F> LogNormal<F>
Sourcepub fn new(mu: F, sigma: F) -> Result<LogNormal<F>, Error>
pub fn new(mu: F, sigma: F) -> Result<LogNormal<F>, Error>
Construct, from (log-space) mean and standard deviation
Parameters are the “standard” log-space measures (these are the mean and standard deviation of the logarithm of samples):
mu(μ, unrestricted) is the mean of the underlying distributionsigma(σ, must be finite) is the standard deviation of the underlying Normal distribution
Sourcepub fn from_mean_cv(mean: F, cv: F) -> Result<LogNormal<F>, Error>
pub fn from_mean_cv(mean: F, cv: F) -> Result<LogNormal<F>, Error>
Construct, from (linear-space) mean and coefficient of variation
Parameters are linear-space measures:
- mean (
μ > 0) is the (real) mean of the distribution - coefficient of variation (
cv = σ / μ, requiringcv ≥ 0) is a standardized measure of dispersion
As a special exception, μ = 0, cv = 0 is allowed (samples are -inf).
Sourcepub fn from_zscore(&self, zscore: F) -> F
pub fn from_zscore(&self, zscore: F) -> F
Sample from a z-score
This may be useful for generating correlated samples x1 and x2
from two different distributions, as follows.
let mut rng = rand::rng();
let z = StandardNormal.sample(&mut rng);
let x1 = LogNormal::from_mean_cv(3.0, 1.0).unwrap().from_zscore(z);
let x2 = LogNormal::from_mean_cv(2.0, 4.0).unwrap().from_zscore(z);