LogNormal (FSharp.Stats)

LogNormal Type

Namespace: FSharp.Stats.Distributions.Continuous
Assembly: FSharp.Stats.dll
Base Type: obj

Log-Normal distribution.

Static members

Static member	Description
`LogNormal.CDF(mu) (sigma) (x)` Full Usage: `LogNormal.CDF(mu) (sigma) (x)` Parameters: mu : `float` - sigma : `float` - x : `float` - Returns: `float`	Computes the cumulative distribution function. mu : `float` sigma : `float` x : `float` Returns: `float` Example
`LogNormal.CheckParam(mu) (sigma)` Full Usage: `LogNormal.CheckParam(mu) (sigma)` Parameters: mu : `float` sigma : `float`	mu : `float` sigma : `float`
`LogNormal.Estimate(samples)` Full Usage: `LogNormal.Estimate(samples)` Parameters: samples : `seq<float>` - Returns: `ContinuousDistribution<float, float>`	Estimates the log-normal distribution parameters from sample data with maximum-likelihood. samples : `seq<float>` Returns: `ContinuousDistribution<float, float>` Example
`LogNormal.Init(mu) (sigma)` Full Usage: `LogNormal.Init(mu) (sigma)` Parameters: mu : `float` - sigma : `float` - Returns: `ContinuousDistribution<float, float>`	Initializes a Normal distribution mu : `float` sigma : `float` Returns: `ContinuousDistribution<float, float>` Example
`LogNormal.InvCDF(mu) (sigma) (x)` Full Usage: `LogNormal.InvCDF(mu) (sigma) (x)` Parameters: mu : `float` - sigma : `float` - x : `float` - Returns: `float`	Computes the inverse cumulative distribution function (quantile function). mu : `float` sigma : `float` x : `float` Returns: `float` Example
`LogNormal.Mean(mu) (sigma)` Full Usage: `LogNormal.Mean(mu) (sigma)` Parameters: mu : `float` - sigma : `float` - Returns: `float`	Computes the mean. mu : `float` sigma : `float` Returns: `float` Example
`LogNormal.Mode(mu) (sigma)` Full Usage: `LogNormal.Mode(mu) (sigma)` Parameters: mu : `float` - sigma : `float` - Returns: `float`	Computes the mode. mu : `float` sigma : `float` Returns: `float` Example
`LogNormal.PDF(mu) (sigma) (x)` Full Usage: `LogNormal.PDF(mu) (sigma) (x)` Parameters: mu : `float` - sigma : `float` - x : `float` - Returns: `float`	Computes the probability density function. mu : `float` sigma : `float` x : `float` Returns: `float` Example
`LogNormal.Sample(mu) (sigma)` Full Usage: `LogNormal.Sample(mu) (sigma)` Parameters: mu : `float` - sigma : `float` - Returns: `float`	Produces a random sample using the current random number generator (from GetSampleGenerator()). mu : `float` sigma : `float` Returns: `float` Example
`LogNormal.StandardDeviation(mu) (sigma)` Full Usage: `LogNormal.StandardDeviation(mu) (sigma)` Parameters: mu : `float` - sigma : `float` - Returns: `float`	Computes the standard deviation. mu : `float` sigma : `float` Returns: `float` Example
`LogNormal.Support(mu) (sigma)` Full Usage: `LogNormal.Support(mu) (sigma)` Parameters: mu : `float` - sigma : `float` - Returns: `Interval<float>`	Returns the support of the log normal distribution: [0, Positive Infinity). mu : `float` sigma : `float` Returns: `Interval<float>` Example
`LogNormal.ToString(mu) (sigma)` Full Usage: `LogNormal.ToString(mu) (sigma)` Parameters: mu : `float` - sigma : `float` - Returns: `string`	A string representation of the distribution. mu : `float` sigma : `float` Returns: `string` Example
`LogNormal.Variance(mu) (sigma)` Full Usage: `LogNormal.Variance(mu) (sigma)` Parameters: mu : `float` - sigma : `float` - Returns: `float`	Computes the variance. mu : `float` sigma : `float` Returns: `float` Example