`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>

source#### pub 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 distribution`sigma`

(`σ`

, must be finite) is the standard deviation of the underlying Normal distribution

source#### pub 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`

, requiring`cv ≥ 0`

) is a standardized measure of dispersion

As a special exception, `μ = 0, cv = 0`

is allowed (samples are `-inf`

).

source#### pub 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);
```

## Trait Implementations§

source§### impl<'de, F> Deserialize<'de> for LogNormal<F>

### impl<'de, F> Deserialize<'de> for LogNormal<F>

source§#### fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,

#### fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,

source§### impl<F> Distribution<F> for LogNormal<F>

### impl<F> Distribution<F> for LogNormal<F>

### impl<F> Copy for LogNormal<F>

### impl<F> StructuralPartialEq for LogNormal<F>

## Auto Trait Implementations§

### impl<F> Freeze for LogNormal<F>where
F: Freeze,

### impl<F> RefUnwindSafe for LogNormal<F>where
F: RefUnwindSafe,

### impl<F> Send for LogNormal<F>where
F: Send,

### impl<F> Sync for LogNormal<F>where
F: Sync,

### impl<F> Unpin for LogNormal<F>where
F: Unpin,

### impl<F> UnwindSafe for LogNormal<F>where
F: UnwindSafe,

## Blanket Implementations§

source§### impl<T> BorrowMut<T> for Twhere
T: ?Sized,

### impl<T> BorrowMut<T> for Twhere
T: ?Sized,

source§#### fn borrow_mut(&mut self) -> &mut T

#### fn borrow_mut(&mut self) -> &mut T

source§### impl<T> CloneToUninit for Twhere
T: Clone,

### impl<T> CloneToUninit for Twhere
T: Clone,

source§#### unsafe fn clone_to_uninit(&self, dst: *mut T)

#### unsafe fn clone_to_uninit(&self, dst: *mut T)

`clone_to_uninit`

)