NegativeBinomial_failures (FSharp.Stats) | FSharp.Stats

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` - The number of success states p : `float` - The probability of each independent bernoulli trial 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. r : `int` The number of success states p : `float` The probability of each independent bernoulli trial 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