Multinomial (FSharp.Stats)

Multinomial Type

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

Static members

Static member	Description
`Multinomial.CDF(p) (n) (x)` Full Usage: `Multinomial.CDF(p) (n) (x)` Parameters: p : `vector` n : `int` x : `float` Returns: `'a`	Computes the cumulative distribution. p : `vector` n : `int` x : `float` Returns: `'a`
`Multinomial.CheckParam(p) (n)` Full Usage: `Multinomial.CheckParam(p) (n)` Parameters: p : `vector` n : `int`	p : `vector` n : `int`
`Multinomial.Mean(p) (n)` Full Usage: `Multinomial.Mean(p) (n)` Parameters: p : `vector` - vector of event probabilities in each trial n : `int` - number of trails Returns: `Vector<float>`	Computes the mean vector p : `vector` vector of event probabilities in each trial n : `int` number of trails Returns: `Vector<float>`
`Multinomial.PMF(p) (x)` Full Usage: `Multinomial.PMF(p) (x)` Parameters: p : `vector` x : `Vector<int>` Returns: `float`	p : `vector` x : `Vector<int>` Returns: `float`
`Multinomial.PMF_Unchecked(p) (x)` Full Usage: `Multinomial.PMF_Unchecked(p) (x)` Parameters: p : `vector` - vector of event probabilities in each trial x : `Vector<int>` - vector of observed successes Returns: `float` Probability of observing the exact outcome of k's with given p's.	The probability mass function (PMF) for the multinomial distribution describes the probability of observing a specific set of outcomes in a series of independent trials with different categories. P(K_i = k_i) n (sum of x's) must be smaller than 170 to not result in infinity due to factorial calculations p : `vector` vector of event probabilities in each trial x : `Vector<int>` vector of observed successes Returns: `float` Probability of observing the exact outcome of k's with given p's. Example `open FSharp.Stats open FSharp.Stats.Distributions.Discrete let p = vector [0.3; 0.5; 0.2] let x = Vector.Generic.ofArray [\|2; 4; 1\|] let pdf = Multinomial.PMF p x // result: 0.118125`
`Multinomial.Sample(p) (n)` Full Usage: `Multinomial.Sample(p) (n)` Parameters: p : `vector` - vector of event probabilities in each trial n : `int` - number of trails Returns: `'a`	Produces a random sample using the current random number generator (from GetSampleGenerator()). p : `vector` vector of event probabilities in each trial n : `int` number of trails Returns: `'a` Example
`Multinomial.StandardDeviation(p) (n)` Full Usage: `Multinomial.StandardDeviation(p) (n)` Parameters: p : `vector` - vector of event probabilities in each trial n : `int` - number of trails Returns: `Vector<float>`	Computes the standard deviation. p : `vector` vector of event probabilities in each trial n : `int` number of trails Returns: `Vector<float>`
`Multinomial.Support(p) (n)` Full Usage: `Multinomial.Support(p) (n)` Parameters: p : `vector` - vector of event probabilities in each trial n : `int` - number of trails Returns: `Vector<Interval<int>>`	Returns the support of the Multinomial distribution: for each x: [0, n]. p : `vector` vector of event probabilities in each trial n : `int` number of trails Returns: `Vector<Interval<int>>` Example
`Multinomial.ToString(p) (n)` Full Usage: `Multinomial.ToString(p) (n)` Parameters: p : `vector` - vector of event probabilities in each trial n : `int` - number of trails Returns: `string`	A string representation of the distribution. p : `vector` vector of event probabilities in each trial n : `int` number of trails Returns: `string`
`Multinomial.Variance(p) (n)` Full Usage: `Multinomial.Variance(p) (n)` Parameters: p : `vector` - vector of event probabilities in each trial n : `int` - number of trails Returns: `Vector<float>`	Computes the variance vector p : `vector` vector of event probabilities in each trial n : `int` number of trails Returns: `Vector<float>`