MultivariateNormal (FSharp.Stats)

MultivariateNormal Type

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

multivariate normal distribution.

Static members

Static member	Description
`MultivariateNormal.CDF(mu) (sigma) (x)` Full Usage: `MultivariateNormal.CDF(mu) (sigma) (x)` Parameters: mu : `vector` sigma : `matrix` x : `vector` Returns: `'a`	Computes the cumulative distribution function. mu : `vector` sigma : `matrix` x : `vector` Returns: `'a`
`MultivariateNormal.Mean(mu) (sigma)` Full Usage: `MultivariateNormal.Mean(mu) (sigma)` Parameters: mu : `vector` sigma : `matrix` Returns: `vector`	Computes the mean. mu : `vector` sigma : `matrix` Returns: `vector`
`MultivariateNormal.PDF(mu) (sigma) (x)` Full Usage: `MultivariateNormal.PDF(mu) (sigma) (x)` Parameters: mu : `vector` sigma : `matrix` x : `vector` Returns: `float`	Computes the probability density function. mu : `vector` sigma : `matrix` x : `vector` Returns: `float`
`MultivariateNormal.Sample(mu) (sigma)` Full Usage: `MultivariateNormal.Sample(mu) (sigma)` Parameters: mu : `vector` sigma : `matrix` Returns: `Vector<float>`	Produces a random sample using the current random number generator (from GetSampleGenerator()). mu : `vector` sigma : `matrix` Returns: `Vector<float>`
`MultivariateNormal.StandardDeviation(mu) (sigma)` Full Usage: `MultivariateNormal.StandardDeviation(mu) (sigma)` Parameters: mu : `vector` sigma : `matrix` Returns: `'a`	Computes the standard deviation. mu : `vector` sigma : `matrix` Returns: `'a`
`MultivariateNormal.Variance(mu) (sigma)` Full Usage: `MultivariateNormal.Variance(mu) (sigma)` Parameters: mu : `vector` sigma : `matrix` Returns: `'a`	Computes the variance. mu : `vector` sigma : `matrix` Returns: `'a`
`MultivariateNormal.init mu sigma` Full Usage: `MultivariateNormal.init mu sigma` Parameters: mu : `vector` sigma : `matrix` Returns: `Distribution<vector, vector>`	Initializes a multivariate normal distribution with mean mu and covariance matrix sigma mu : `vector` sigma : `matrix` Returns: `Distribution<vector, vector>`