Normal (FSharp.Stats)

Normal Type

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

Normal distribution.

Static members

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