NegativeBinomial_failures (FSharp.Stats)

NegativeBinomial_failures Type

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

The distribution of the number of failures (k) before the rth success in repeated independent bernoulli trials with individual probability p.

Static members

Static member	Description
`NegativeBinomial_failures.CDF(r) (p) (k)` Full Usage: `NegativeBinomial_failures.CDF(r) (p) (k)` Parameters: r : `int` p : `float` k : `float` Returns: `float` Probability of requiring a maximum of k failures when r successes are required at a individual probability of p	Computes the cumulative distribution function. P(X lower or equal then k) X=the number of failures before the rth succeess r : `int` p : `float` k : `float` Returns: `float` Probability of requiring a maximum of k failures when r successes are required at a individual probability of p
`NegativeBinomial_failures.CheckParam(r) (p)` Full Usage: `NegativeBinomial_failures.CheckParam(r) (p)` Parameters: r : `int` p : `float`	r : `int` p : `float`
`NegativeBinomial_failures.Init(r) (p)` Full Usage: `NegativeBinomial_failures.Init(r) (p)` Parameters: r : `int` - p : `float` - Returns: `DiscreteDistribution<float, int>`	Initializes a negative binomial distribution. The negative binomial distribution is a discrete probability distribution that models the number of failures needed k to get the rth success in repeated independent Bernoulli trials with probability p. The number of success states The probability of each independent bernoulli trial The number of failures before the rth success r : `int` p : `float` Returns: `DiscreteDistribution<float, int>` Example
`NegativeBinomial_failures.InvCDF(r) (p) (k)` Full Usage: `NegativeBinomial_failures.InvCDF(r) (p) (k)` Parameters: r : `int` p : `float` k : `'a` Returns: `'b`	Computes the inverse cumulative distribution function (quantile function). X=the number of failures before the rth succeess r : `int` p : `float` k : `'a` Returns: `'b`
`NegativeBinomial_failures.Mean(r) (p)` Full Usage: `NegativeBinomial_failures.Mean(r) (p)` Parameters: r : `int` - p : `float` - Returns: `float`	Computes the mean. r : `int` p : `float` Returns: `float` Example
`NegativeBinomial_failures.Mode(r) (p)` Full Usage: `NegativeBinomial_failures.Mode(r) (p)` Parameters: r : `int` - p : `float` - Returns: `int`	Computes the mode. Number of failures with the highest probability to obtain r successes with the last trial. r : `int` p : `float` Returns: `int` Example
`NegativeBinomial_failures.PMF(r) (p) (k)` Full Usage: `NegativeBinomial_failures.PMF(r) (p) (k)` Parameters: r : `int` p : `float` k : `int` Returns: `float` Probability of requiring k failures when r successes are required at a individual probability of p	Computes the probability mass function r : `int` p : `float` k : `int` Returns: `float` Probability of requiring k failures when r successes are required at a individual probability of p
`NegativeBinomial_failures.PMFLn(r) (p) (k)` Full Usage: `NegativeBinomial_failures.PMFLn(r) (p) (k)` Parameters: r : `int` p : `float` k : `int` Returns: `float` log probability of requiring k failures when r successes are required at a individual probability of p	Computes the log probability mass function r : `int` p : `float` k : `int` Returns: `float` log probability of requiring k failures when r successes are required at a individual probability of p
`NegativeBinomial_failures.Sample(r) (p)` Full Usage: `NegativeBinomial_failures.Sample(r) (p)` Parameters: r : `int` - p : `float` - Returns: `'a`	Produces a random sample using the current random number generator (from GetSampleGenerator()). r : `int` p : `float` Returns: `'a` Example
`NegativeBinomial_failures.SampleUnchecked(r) (p)` Full Usage: `NegativeBinomial_failures.SampleUnchecked(r) (p)` Parameters: r : `'a` - p : `'b` - Returns: `'c`	Produces a random sample using the current random number generator (from GetSampleGenerator()). r : `'a` p : `'b` Returns: `'c` Example
`NegativeBinomial_failures.StandardDeviation(r) (p)` Full Usage: `NegativeBinomial_failures.StandardDeviation(r) (p)` Parameters: r : `int` - p : `float` - Returns: `float`	Computes the standard deviation. r : `int` p : `float` Returns: `float` Example
`NegativeBinomial_failures.Support(r) (p)` Full Usage: `NegativeBinomial_failures.Support(r) (p)` Parameters: r : `int` - p : `float` - Returns: `int * int`	Returns the support of the NegativeBinomial distribution: [r, max Int32). r : `int` p : `float` Returns: `int * int` Example
`NegativeBinomial_failures.ToString(r) (p)` Full Usage: `NegativeBinomial_failures.ToString(r) (p)` Parameters: r : `int` - p : `float` - Returns: `string`	A string representation of the distribution. r : `int` p : `float` Returns: `string` Example
`NegativeBinomial_failures.Variance(r) (p)` Full Usage: `NegativeBinomial_failures.Variance(r) (p)` Parameters: r : `int` - p : `float` - Returns: `float`	Computes the variance. r : `int` p : `float` Returns: `float` Example