Poisson (FSharp.Stats)

Poisson Type

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

Poisson distribution

Static members

Static member	Description
`Poisson.CDF(lambda) (k)` Full Usage: `Poisson.CDF(lambda) (k)` Parameters: lambda : `float` - k : `float` - Returns: `float`	Computes the cumulative distribution function at x, i.e. P(X <= x). lambda : `float` k : `float` Returns: `float` Example
`Poisson.CheckParam(lambda)` Full Usage: `Poisson.CheckParam(lambda)` Parameters: lambda : `float`	lambda : `float`
`Poisson.Entropy(lambda)` Full Usage: `Poisson.Entropy(lambda)` Parameters: lambda : `float` Returns: `float`	Computes the entropy for this distribution. It's an approximation and better for large lambda. lambda : `float` Returns: `float`
`Poisson.Estimate(observations, ?weights)` Full Usage: `Poisson.Estimate(observations, ?weights)` Parameters: observations : `float[]` ?weights : `float[]` Returns: `DiscreteDistribution<float, int>`	Estimates a new Poisson distribution from a given set of observations. observations : `float[]` ?weights : `float[]` Returns: `DiscreteDistribution<float, int>`
`Poisson.Fit(observations, ?weights)` Full Usage: `Poisson.Fit(observations, ?weights)` Parameters: observations : `float[]` ?weights : `float[]` Returns: `float`	Fits the underlying distribution to a given set of observations. observations : `float[]` ?weights : `float[]` Returns: `float`
`Poisson.Init(lambda)` Full Usage: `Poisson.Init(lambda)` Parameters: lambda : `float` - Returns: `DiscreteDistribution<float, int>`	Initializes a Binomial distribution lambda : `float` Returns: `DiscreteDistribution<float, int>` Example
`Poisson.InvCDF(lambda) (k)` Full Usage: `Poisson.InvCDF(lambda) (k)` Parameters: lambda : `float` - k : `'a` - Returns: `'b`	Computes the inverse cumulative distribution function (quantile function). lambda : `float` k : `'a` Returns: `'b` Example
`Poisson.Mean(lambda)` Full Usage: `Poisson.Mean(lambda)` Parameters: lambda : `float` - Returns: `float`	Computes the mean. lambda : `float` Returns: `float` Example
`Poisson.Mode(lambda)` Full Usage: `Poisson.Mode(lambda)` Parameters: lambda : `float` - Returns: `int`	Computes the mode. lambda : `float` Returns: `int` Example
`Poisson.PMF(lambda) (k)` Full Usage: `Poisson.PMF(lambda) (k)` Parameters: lambda : `float` k : `int` Returns: `float`	Computes the probability density function at k, i.e. P(K = k) lambda : `float` k : `int` Returns: `float`
`Poisson.Sample(lambda)` Full Usage: `Poisson.Sample(lambda)` Parameters: lambda : `float` - Returns: `int`	Produces a random sample using the current random number generator (from GetSampleGenerator()). lambda : `float` Returns: `int` Example
`Poisson.StandardDeviation(lambda)` Full Usage: `Poisson.StandardDeviation(lambda)` Parameters: lambda : `float` - Returns: `float`	Computes the standard deviation. lambda : `float` Returns: `float` Example
`Poisson.Support(lambda)` Full Usage: `Poisson.Support(lambda)` Parameters: lambda : `float` - Returns: `Interval<int>`	Returns the support interval for this distribution. lambda : `float` Returns: `Interval<int>` Example
`Poisson.ToString(lambda)` Full Usage: `Poisson.ToString(lambda)` Parameters: lambda : `float` - Returns: `string`	A string representation of the distribution. lambda : `float` Returns: `string` Example
`Poisson.Variance(lambda)` Full Usage: `Poisson.Variance(lambda)` Parameters: lambda : `float` - Returns: `float`	Computes the variance. lambda : `float` Returns: `float` Example