Beta (FSharp.Stats) | FSharp.Stats

FSharp.Stats

Beta Type

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

Beta distribution

Static members

Static member	Description
`Beta.CDF(alpha) (beta) (x)` Full Usage: `Beta.CDF(alpha) (beta) (x)` Parameters: alpha : `float` - beta : `float` - x : `float` - Returns: `float`	Computes the cumulative distribution function. alpha : `float` beta : `float` x : `float` Returns: `float` Example
`Beta.CheckParam(alpha) (beta)` Full Usage: `Beta.CheckParam(alpha) (beta)` Parameters: alpha : `float` beta : `float`	alpha : `float` beta : `float`
`Beta.Estimate(observations, ?weights)` Full Usage: `Beta.Estimate(observations, ?weights)` Parameters: observations : `float[]` ?weights : `float[]` Returns: `ContinuousDistribution<float, float>`	Estimates a new Beta distribution from a given set of observations. observations : `float[]` ?weights : `float[]` Returns: `ContinuousDistribution<float, float>`
`Beta.Fit(observations, ?weights)` Full Usage: `Beta.Fit(observations, ?weights)` Parameters: observations : `float[]` ?weights : `float[]` Returns: `float * float`	Fits the underlying distribution to a given set of observations. observations : `float[]` ?weights : `float[]` Returns: `float * float`
`Beta.Init(alpha) (beta)` Full Usage: `Beta.Init(alpha) (beta)` Parameters: alpha : `float` - beta : `float` - Returns: `ContinuousDistribution<float, float>`	Initializes a Beta distribution alpha : `float` beta : `float` Returns: `ContinuousDistribution<float, float>` Example
`Beta.InvCDF(alpha) (beta) (x)` Full Usage: `Beta.InvCDF(alpha) (beta) (x)` Parameters: alpha : `float` - beta : `float` - x : `'a` - Returns: `'b`	Computes the inverse cumulative distribution function (quantile function). alpha : `float` beta : `float` x : `'a` Returns: `'b` Example
`Beta.Mean(alpha) (beta)` Full Usage: `Beta.Mean(alpha) (beta)` Parameters: alpha : `float` - beta : `float` - Returns: `float`	Computes the mean. alpha : `float` beta : `float` Returns: `float` Example
`Beta.Mode(alpha) (beta)` Full Usage: `Beta.Mode(alpha) (beta)` Parameters: alpha : `float` - beta : `float` - Returns: `float`	Computes the mode. alpha : `float` beta : `float` Returns: `float` Example
`Beta.PDF(alpha) (beta) (x)` Full Usage: `Beta.PDF(alpha) (beta) (x)` Parameters: alpha : `float` beta : `float` x : `float` Returns: `float`	Computes the probability density function. Calls exp(PDFLn) if alpha,beta > 80 alpha : `float` beta : `float` x : `float` Returns: `float`
`Beta.PDFLn(alpha) (beta) (x)` Full Usage: `Beta.PDFLn(alpha) (beta) (x)` Parameters: alpha : `float` - beta : `float` - x : `float` - Returns: `float`	Computes the log probability density function. alpha : `float` beta : `float` x : `float` Returns: `float` Example
`Beta.Sample(alpha) (beta)` Full Usage: `Beta.Sample(alpha) (beta)` Parameters: alpha : `float` - beta : `float` - Returns: `float`	Produces a random sample using the current random number generator (from GetSampleGenerator()). alpha : `float` beta : `float` Returns: `float` Example
`Beta.StandardDeviation(alpha) (beta)` Full Usage: `Beta.StandardDeviation(alpha) (beta)` Parameters: alpha : `float` - beta : `float` - Returns: `float`	Computes the standard deviation. alpha : `float` beta : `float` Returns: `float` Example
`Beta.Support(alpha) (beta)` Full Usage: `Beta.Support(alpha) (beta)` Parameters: alpha : `float` - beta : `float` - Returns: `Interval<float>`	Returns the support of the exponential distribution: [0.0, 1.0). alpha : `float` beta : `float` Returns: `Interval<float>` Example
`Beta.ToString(alpha) (beta)` Full Usage: `Beta.ToString(alpha) (beta)` Parameters: alpha : `float` - beta : `float` - Returns: `string`	A string representation of the distribution. alpha : `float` beta : `float` Returns: `string` Example
`Beta.Variance(alpha) (beta)` Full Usage: `Beta.Variance(alpha) (beta)` Parameters: alpha : `float` - beta : `float` - Returns: `float`	Computes the variance. alpha : `float` beta : `float` Returns: `float` Example