KSLCore/ksl.utilities.distributions/DUniform

DUniform

class DUniform(min: Int = 0, max: Int = 1, name: String? = null) : Distribution, DiscreteDistributionIfc, RVParametersTypeIfc

Models discrete random variables that are uniformly distributed over a contiguous range of integers. the lower limit must be < upper limit

Parameters

min

The lower limit of the range

max

The upper limit of the range

name

an optional name/label

Constructors

DUniform

constructor(parameters: DoubleArray)

Constructs a discrete uniform where parameter0 is the lower limit and parameter1 is the upper limit of the range. the lower limit must be < upper limit

constructor(range: IntRange)

constructor(min: Int = 0, max: Int = 1, name: String? = null)

Properties

open override val id: Int

label

open override var label: String?

maximum

var maximum: Int

The distribution's upper limit

minimum

var minimum: Int

The distribution's lower limit

name

open override val name: String

parametrizedRVClass

open override val parametrizedRVClass: KClass<out ParameterizedRV>

randomVariable

open val randomVariable: RVariableIfc

range

val range: Int

The discrete maximum - minimum + 1

rvParameters

open override val rvParameters: RVParameters

the parameters for this type of random variable

Functions

cdf

open fun cdf(x: DoubleArray): DoubleArray

Returns an array of probabilities each representing F(x_i). The CDF is evaluated for each point in the input array x and the probabilities are returned in the returned array.

open fun cdf(interval: Interval): Double

Returns the probability of being in the interval, F(upper limit) - F(lower limit) Be careful, this is Pr{lower limit < = X < = upper limit} which includes the lower limit and has implications if the distribution is discrete

open fun cdf(x1: Double, x2: Double): Double

Returns the Pr{x1 <= X <= x2} for the distribution. Be careful, this is Pr{x1 <= X <= x2} which includes the lower limit and has implications if the distribution is discrete

open override fun cdf(x: Double): Double

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

complementaryCDF

open fun complementaryCDF(x: Double): Double

Computes the complementary cumulative probability distribution function for given value of x. This is P{X > x}

instance

open override fun instance(): DUniform

invCDF

open override fun invCDF(p: Double): Double

Provides the inverse cumulative distribution function for the distribution

open fun invCDF(probabilities: DoubleArray): DoubleArray

Computes x_p where P(X <= x_p) = p for the supplied array of probabilities. Requires that the values within the supplied array are in (0,1)

mean

open override fun mean(): Double

Returns the mean or expected value of a distribution

parameters

open override fun parameters(): DoubleArray

Gets the parameters for the distribution where parameters0 is the lower limit and parameters1 is the upper limit of the range.

open override fun parameters(params: DoubleArray)

Sets the parameters for the distribution where parameters0 is the lower limit and parameters1 is the upper limit of the range. the lower limit must be < upper limit

pmf

open override fun pmf(i: Int): Double

Returns the probability associated with x

open fun pmf(x: Double): Double

If x is not an integer value, then the probability must be zero otherwise pmf(int x) is used to determine the probability

open fun pmf(range: IntRange): Map<Int, Double>

Computes the probabilities associated with the range and returns the value and the probability as a map with the integer value as the key and the probability as the related value.

randomVariable