KSLCore/ksl.utilities.distributions/Binomial

Binomial

class Binomial(pSuccess: Double = 0.5, nTrials: Int = 1, name: String? = null) : Distribution, DiscretePMFInRangeDistributionIfc, LossFunctionDistributionIfc, ThirdOrderLossFunctionIfc, RVParametersTypeIfc(source)

Constructors

Binomial

constructor(pSuccess: Double = 0.5, nTrials: Int = 1, name: String? = null)

Types

Companion

object Companion

Properties

numTrials

var numTrials: Int

The number of trials

probOfSuccess

var probOfSuccess: Double

The probability of success

useRecursiveAlgorithm

var useRecursiveAlgorithm: Boolean

indicates whether pmf and cdf calculations are done by recursive (iterative) algorithm based on logarithms or via beta incomplete function and binomial coefficients.

Functions

cdf

open override fun cdf(x: Double): Double

Returns the F(x) = Pr{X <= x} where F represents the cumulative distribution function

fun cdf(x: Int): Double

firstOrderLossFunction

open override fun firstOrderLossFunction(x: Double): Double

G1(x) = Emax(X - x, 0).

instance

open override fun instance(): Binomial

invCDF

open override fun invCDF(p: Double): Double

Provides the inverse cumulative distribution function for the distribution

mean

open override fun mean(): Double

Returns the mean or expected value of a distribution

parameters

open override fun parameters(): DoubleArray

Gets the parameters

open override fun parameters(params: DoubleArray)

Sets the parameters

pmf

open override fun pmf(i: Int): Double

probIn

open override fun probIn(range: IntRange): Double

Computes the sum of the probabilities over the provided range. If the range is closed a..b then the end point b is included in the sum. If the range is open a..<b then the point b is not included in the sum.

randomVariable

open override fun randomVariable(streamNumber: Int, streamProvider: RNStreamProviderIfc): BinomialRV

Promises to return a random variable that uses the supplied stream number using the supplied stream provider

secondOrderLossFunction

open override fun secondOrderLossFunction(x: Double): Double

G2(x) = (1/2) * Emax(X - x, 0) * max(X - x - 1, 0).

thirdOrderLossFunction

open override fun thirdOrderLossFunction(x: Double): Double

G3(x) = (1/6) * Emax(X - x, 0) * max(X - x - 1, 0) * max(X - x - 2, 0).

toString

open override fun toString(): String

variance

open override fun variance(): Double

Returns the variance of the distribution if defined