RandomVariableInterface (finmath lib 1.3.4 API)

All Superinterfaces:

Serializable

All Known Subinterfaces:

RandomVariableAccumulatorInterface

All Known Implementing Classes:

RandomVariable, RandomVariableLowMemory, RandomVariableMutableClone
```
public interface RandomVariableInterface
extends Serializable
```
This interface describes the methods implemented by an immutable random variable, i.e. methods that leave a random variable unchanged (immutable). This is used to ensure that arguments or return values are not changed. For C++ guys: In C++ you could achieve this by making a return value const.
IMPORTANT: As of version 1.3 / revision 487 the design of RandomVariable, RandomVariableInterface has changed: All methods of RandomVariable leave the object immutable and the interface ImmutableRandomVariableInterface has been renamed to RandomVariableInterface. Your code remains compatible if you perform the following changes:
- Change calls to RandomVariableInterface objects value like value.mult(argument); to value = value.mult(argument);
- Remove calls to getMutableCopy() since they are no longer needed.
- Remove wrapping in RandomVariableMutableClone since they are no longer needed.
The change has some performance impact, however, the original performance may be achieved again via the use of Java 8 lambdas and the concept of the RandomVariableAccumulatorInterface.
Version:

1.5

Author:

Christian Fries

Method Summary

All Methods Instance Methods Abstract Methods Deprecated Methods
Modifier and Type	Method and Description
`RandomVariableInterface`	`abs()` Applies x → Math.abs(x), i.e. x → \|x\| to this random variable.
`RandomVariableInterface`	`accrue(RandomVariableInterface rate, double periodLength)` Applies x → x * (1.0 + rate * periodLength) to this random variable.
`RandomVariableInterface`	`add(double value)` Applies x → x + value to this random variable.
`RandomVariableInterface`	`add(RandomVariableInterface randomVariable)` Applies x → x+randomVariable to this random variable.
`RandomVariableInterface`	`addProduct(RandomVariableInterface factor1, double factor2)` Applies x → x + factor1 * factor2
`RandomVariableInterface`	`addProduct(RandomVariableInterface factor1, RandomVariableInterface factor2)` Applies x → x + factor1 * factor2
`RandomVariableInterface`	`addRatio(RandomVariableInterface numerator, RandomVariableInterface denominator)` Applies x → x + numerator / denominator
`RandomVariableInterface`	`apply(UnivariateFunction function)` Applies x → function.value(x) to this random variable.
`RandomVariableInterface`	`barrier(RandomVariableInterface trigger, RandomVariableInterface valueIfTriggerNonNegative, double valueIfTriggerNegative)` Applies x → (trigger ≥ 0 ?
`RandomVariableInterface`	`barrier(RandomVariableInterface trigger, RandomVariableInterface valueIfTriggerNonNegative, RandomVariableInterface valueIfTriggerNegative)` Applies x → (trigger ≥ 0 ?
`RandomVariableInterface`	`cache()` Return a cacheable version of this object (often a self-reference).
`RandomVariableInterface`	`cap(double cap)` Applies x → min(x,cap) to this random variable.
`RandomVariableInterface`	`cap(RandomVariableInterface cap)` Applies x → min(x,cap) to this random variable.
`RandomVariableInterface`	`cos()` Applies x → cos(x) to this random variable.
`RandomVariableInterface`	`discount(RandomVariableInterface rate, double periodLength)` Applies x → x / (1.0 + rate * periodLength) to this random variable.
`RandomVariableInterface`	`div(double value)` Applies x → x / value to this random variable.
`RandomVariableInterface`	`div(RandomVariableInterface randomVariable)` Applies x → x/randomVariable to this random variable.
`boolean`	`equals(RandomVariableInterface randomVariable)` Compare this random variable with a given one
`RandomVariableInterface`	`exp()` Applies x → exp(x) to this random variable.
`RandomVariableInterface`	`floor(double floor)` Applies x → max(x,floor) to this random variable.
`RandomVariableInterface`	`floor(RandomVariableInterface floor)` Applies x → max(x,floor) to this random variable.
`double`	`get(int pathOrState)` Evaluate at a given path or state.
`double`	`getAverage()` Returns the expectation of this random variable.
`double`	`getAverage(RandomVariableInterface probabilities)` Returns the expectation of this random variable for a given probability measure (weight).
`double`	`getFiltrationTime()` Returns the filtration time.
`double[]`	`getHistogram(double[] intervalPoints)` Generates a Histogram based on the realizations stored in this random variable.
`double[][]`	`getHistogram(int numberOfPoints, double standardDeviations)` Generates a histogram based on the realizations stored in this random variable using interval points calculated from the arguments, see also `getHistogram(double[])`.
`double`	`getMax()` Returns the maximum value attained by this random variable.
`double`	`getMin()` Returns the minimum value attained by this random variable.
`RandomVariableInterface`	`getMutableCopy()` Deprecated.
`double`	`getQuantile(double quantile)` Returns the quantile value for this given random variable, i.e., the value x such that P(this < x) = quantile, where P denotes the probability measure.
`double`	`getQuantile(double quantile, RandomVariableInterface probabilities)` Returns the quantile value for this given random variable, i.e., the value x such that P(this < x) = quantile, where P denotes the probability measure.
`double`	`getQuantileExpectation(double quantileStart, double quantileEnd)` Returns the expectation over a quantile for this given random variable.
`double[]`	`getRealizations()` Returns a vector representing the realization of this random variable.
`double[]`	`getRealizations(int numberOfPaths)` Returns the realizations as double array.
`double`	`getStandardDeviation()` Returns the standard deviation of this random variable, i.e., sqrt(V) where V = ((X-m)^2).getAverage() and X = this and m = X.getAverage().
`double`	`getStandardDeviation(RandomVariableInterface probabilities)` Returns the standard deviation of this random variable, i.e., sqrt(V) where V = ((X-m)^2).getAverage(probabilities) and X = this and m = X.getAverage(probabilities).
`double`	`getStandardError()` Returns the standard error (discretization error) of this random variable.
`double`	`getStandardError(RandomVariableInterface probabilities)` Returns the standard error (discretization error) of this random variable.
`double`	`getVariance()` Returns the variance of this random variable, i.e., V where V = ((X-m)^2).getAverage() and X = this and m = X.getAverage().
`double`	`getVariance(RandomVariableInterface probabilities)` Returns the variance of this random variable, i.e., V where V = ((X-m)^2).getAverage(probabilities) and X = this and m = X.getAverage(probabilities).
`RandomVariableInterface`	`invert()` Applies x → 1/x to this random variable.
`boolean`	`isDeterministic()` Check if this random variable is deterministic in the sense that it is represented by a single double value.
`RandomVariableInterface`	`isNaN()` Applies x → (Double.isNaN(x) ?
`RandomVariableInterface`	`log()` Applies x → log(x) to this random variable.
`RandomVariableInterface`	`mult(double value)` Applies x → x * value to this random variable.
`RandomVariableInterface`	`mult(RandomVariableInterface randomVariable)` Applies x → x*randomVariable to this random variable.
`RandomVariableInterface`	`pow(double exponent)` Applies x → pow(x,exponent) to this random variable.
`RandomVariableInterface`	`sin()` Applies x → sin(x) to this random variable.
`int`	`size()` Returns the number of paths or states.
`RandomVariableInterface`	`sqrt()` Applies x → sqrt(x) to this random variable.
`RandomVariableInterface`	`squared()` Applies x → x * x to this random variable.
`RandomVariableInterface`	`sub(double value)` Applies x → x - value to this random variable.
`RandomVariableInterface`	`sub(RandomVariableInterface randomVariable)` Applies x → x-randomVariable to this random variable.
`RandomVariableInterface`	`subRatio(RandomVariableInterface numerator, RandomVariableInterface denominator)` Applies x → x - numerator / denominator

- Method Detail
  - equals
```
boolean equals(RandomVariableInterface randomVariable)
```
    Compare this random variable with a given one
    
    Parameters:
    
    randomVariable - Random variable to compare with.
    
    Returns:
    
    True if this random variable and the given one are equal, otherwise false
  - getFiltrationTime
```
double getFiltrationTime()
```
    Returns the filtration time.
    
    Returns:
    
    The filtration time.
  - get
```
double get(int pathOrState)
```
    Evaluate at a given path or state.
    
    Parameters:
    
    pathOrState - Index of the path or state.
    
    Returns:
    
    Value of this random variable at the given path or state.
  - size
```
int size()
```
    Returns the number of paths or states.
    
    Returns:
    
    Number of paths or states.
  - isDeterministic
```
boolean isDeterministic()
```
    Check if this random variable is deterministic in the sense that it is represented by a single double value. Note that the methods returns false, if the random variable is represented by a vector where each element has the same value.
    
    Returns:
    
    True if this random variable is deterministic.
  - getRealizations
```
double[] getRealizations()
```
    Returns a vector representing the realization of this random variable. This method is merely useful for analysis. Its interpretation depends on the context (Monte-Carlo or lattice). The method does not expose an internal data model.
    
    Returns:
    
    Vector of realizations of this random variable.
  - getRealizations
```
double[] getRealizations(int numberOfPaths)
```
    Returns the realizations as double array. If the random variable is deterministic, then it is expanded to the given number of paths.
    
    Parameters:
    
    numberOfPaths - The number of paths.
    
    Returns:
    
    The realization as double array.
  - getMin
```
double getMin()
```
    Returns the minimum value attained by this random variable.
    
    Returns:
    
    The minimum value.
  - getMax
```
double getMax()
```
    Returns the maximum value attained by this random variable.
    
    Returns:
    
    The maximum value.
  - getAverage
```
double getAverage()
```
    Returns the expectation of this random variable.
    
    Returns:
    
    The average assuming equi-distribution.
  - getAverage
```
double getAverage(RandomVariableInterface probabilities)
```
    Returns the expectation of this random variable for a given probability measure (weight). The result of this method is (mathematically) equivalent to
    this.mult(probabilities).getAverage() / probabilities.getAverage()
    while the internal implementation may differ, e.g. being more efficient by performing multiplication and summation in the same loop.
    
    Parameters:
    
    probabilities - The probability weights.
    
    Returns:
    
    The average assuming the given probability weights.
  - getVariance
```
double getVariance()
```
    Returns the variance of this random variable, i.e., V where V = ((X-m)^2).getAverage() and X = this and m = X.getAverage().
    
    Returns:
    
    The average assuming equi-distribution.
  - getVariance
```
double getVariance(RandomVariableInterface probabilities)
```
    Returns the variance of this random variable, i.e., V where V = ((X-m)^2).getAverage(probabilities) and X = this and m = X.getAverage(probabilities).
    
    Parameters:
    
    probabilities - The probability weights.
    
    Returns:
    
    The average assuming the given probability weights.
  - getStandardDeviation
```
double getStandardDeviation()
```
    Returns the standard deviation of this random variable, i.e., sqrt(V) where V = ((X-m)^2).getAverage() and X = this and m = X.getAverage().
    
    Returns:
    
    The standard deviation assuming equi-distribution.
  - getStandardDeviation
```
double getStandardDeviation(RandomVariableInterface probabilities)
```
    Returns the standard deviation of this random variable, i.e., sqrt(V) where V = ((X-m)^2).getAverage(probabilities) and X = this and m = X.getAverage(probabilities).
    
    Parameters:
    
    probabilities - The probability weights.
    
    Returns:
    
    The standard error assuming the given probability weights.
  - getStandardError
```
double getStandardError()
```
    Returns the standard error (discretization error) of this random variable. For a Monte-Carlo simulation this is 1/Math.sqrt(n) * getStandardDeviation().
    
    Returns:
    
    The standard error assuming equi-distribution.
  - getStandardError
```
double getStandardError(RandomVariableInterface probabilities)
```
    Returns the standard error (discretization error) of this random variable. For a Monte-Carlo simulation this is 1/Math.sqrt(n) * getStandardDeviation(RandomVariableInterface).
    
    Parameters:
    
    probabilities - The probability weights.
    
    Returns:
    
    The standard error assuming the given probability weights.
  - getQuantile
```
double getQuantile(double quantile)
```
    Returns the quantile value for this given random variable, i.e., the value x such that P(this < x) = quantile, where P denotes the probability measure. The method will consider picewise constant values (with constant extrapolation) in the random variable. That is getQuantile(0) and getQuantile(1) will return the largest and smallest value.
    
    Parameters:
    
    quantile - The quantile level.
    
    Returns:
    
    The quantile value assuming equi-distribution.
  - getQuantile
```
double getQuantile(double quantile,
                   RandomVariableInterface probabilities)
```
    Returns the quantile value for this given random variable, i.e., the value x such that P(this < x) = quantile, where P denotes the probability measure.
    
    Parameters:
    
    quantile - The quantile level.
    
    probabilities - The probability weights.
    
    Returns:
    
    The quantile value assuming the given probability weights.
  - getQuantileExpectation
```
double getQuantileExpectation(double quantileStart,
                              double quantileEnd)
```
    Returns the expectation over a quantile for this given random variable. The method will consider picewise constant values (with constant extrapolation) in the random variable. For a ≤ b the method returns (Σ_{a ≤ i ≤ b} x[i]) / (b-a+1), where
    - a = min(max((n+1) * quantileStart - 1, 0, 1);
    - b = min(max((n+1) * quantileEnd - 1, 0, 1);
    - n = this.size();
    For quantileStart > quantileEnd the method returns getQuantileExpectation(quantileEnd, quantileStart).
    Parameters:
    
    quantileStart - Lower bound of the integral.
    
    quantileEnd - Upper bound of the integral.
    
    Returns:
    
    The (conditional) expectation of the values between two quantile levels assuming equi-distribution.
  - getHistogram
```
double[] getHistogram(double[] intervalPoints)
```
    Generates a Histogram based on the realizations stored in this random variable. The returned result array's length is intervalPoints.length+1.
    - The value result[0] equals the relative frequency of values observed in the interval ( -infinity, intervalPoints[0] ].
    - The value result[i] equals the relative frequency of values observed in the interval ( intervalPoints[i-1], intervalPoints[i] ].
    - The value result[n] equals the relative frequency of values observed in the interval ( intervalPoints[n-1], infinity ).
    where n = intervalPoints.length. Note that the intervals are open on the left, closed on the right, i.e., result[i] contains the number of elements x with intervalPoints[i-1] < x ≤ intervalPoints[i]. Thus, is you have a random variable which only takes values contained in the (sorted) array possibleValues, then result = getHistogram(possibleValues) returns an array where result[i] is the relative frequency of occurrence of possibleValues[i]. The sum of result[i] over all i is equal to 1, except for uninitialized random variables where all values are 0.
    Parameters:
    
    intervalPoints - Array of ascending values defining the interval boundaries.
    
    Returns:
    
    A histogram with respect to a provided interval.
  - getHistogram
```
double[][] getHistogram(int numberOfPoints,
                        double standardDeviations)
```
    Generates a histogram based on the realizations stored in this random variable using interval points calculated from the arguments, see also getHistogram(double[]). The interval points are set with equal distance over an the interval of the specified standard deviation. The interval points used are x[i] = mean + alpha[i] * standardDeviations * sigma where
    - i = 0,..., numberOfPoints-1,
    - alpha[i] = (i - (numberOfPoints-1)/2.0) / ((numberOfPoints-1)/2.0),
    - mean = getAverage(),
    - sigma = getStandardDeviation().
    The methods result is an array of two vectors, where result[0] are the intervals center points ('anchor points') and result[1] contains the relative frequency for the interval. The 'anchor point' for the interval (-infinity, x[0]) is x[0] - 1/2 (x[1]-x[0]) and the 'anchor point' for the interval (x[n], infinity) is x[n] + 1/2 (x[n]-x[n-1]). Here n = numberOfPoints is the number of interval points.
    Parameters:
    
    numberOfPoints - The number of interval points.
    
    standardDeviations - The number of standard deviations defining the discretization radius.
    
    Returns:
    
    A histogram, given as double[2][], where result[0] are the center point of the intervals and result[1] is the value of getHistogram(double[]) for the given the interval points. The length of result[0] and result[1] is numberOfPoints+1.
  - cache
```
RandomVariableInterface cache()
```
    Return a cacheable version of this object (often a self-reference). This method should be called when you store the object for later use, i.e., assign it, or when the object is consumed in a function, but later used also in another function.
    
    Returns:
    
    A cacheable version of this object (often a self-reference).
  - apply
```
RandomVariableInterface apply(UnivariateFunction function)
```
    Applies x → function.value(x) to this random variable.
    
    Parameters:
    
    function - A univariate function mapping doubles to doubles.
    
    Returns:
    
    New random variable with the result of the function.
  - cap
```
RandomVariableInterface cap(double cap)
```
    Applies x → min(x,cap) to this random variable. It returns a new random variable with the result.
    
    Parameters:
    
    cap - The cap.
    
    Returns:
    
    New random variable with the result of the function.
  - floor
```
RandomVariableInterface floor(double floor)
```
    Applies x → max(x,floor) to this random variable. It returns a new random variable with the result.
    
    Parameters:
    
    floor - The floor.
    
    Returns:
    
    New random variable with the result of the function.
  - add
```
RandomVariableInterface add(double value)
```
    Applies x → x + value to this random variable. It returns a new random variable with the result.
    
    Parameters:
    
    value - The value to add.
    
    Returns:
    
    New random variable with the result of the function.
  - sub
```
RandomVariableInterface sub(double value)
```
    Applies x → x - value to this random variable.
    
    Parameters:
    
    value - The value to subtract.
    
    Returns:
    
    New random variable with the result of the function.
  - mult
```
RandomVariableInterface mult(double value)
```
    Applies x → x * value to this random variable.
    
    Parameters:
    
    value - The value to multiply.
    
    Returns:
    
    New random variable with the result of the function.
  - div
```
RandomVariableInterface div(double value)
```
    Applies x → x / value to this random variable.
    
    Parameters:
    
    value - The value to divide.
    
    Returns:
    
    New random variable with the result of the function.
  - pow
```
RandomVariableInterface pow(double exponent)
```
    Applies x → pow(x,exponent) to this random variable.
    
    Parameters:
    
    exponent - The exponent.
    
    Returns:
    
    New random variable with the result of the function.
  - squared
```
RandomVariableInterface squared()
```
    Applies x → x * x to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - sqrt
```
RandomVariableInterface sqrt()
```
    Applies x → sqrt(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - exp
```
RandomVariableInterface exp()
```
    Applies x → exp(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - log
```
RandomVariableInterface log()
```
    Applies x → log(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - sin
```
RandomVariableInterface sin()
```
    Applies x → sin(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - cos
```
RandomVariableInterface cos()
```
    Applies x → cos(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - add
```
RandomVariableInterface add(RandomVariableInterface randomVariable)
```
    Applies x → x+randomVariable to this random variable.
    
    Parameters:
    
    randomVariable - A random variable (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - sub
```
RandomVariableInterface sub(RandomVariableInterface randomVariable)
```
    Applies x → x-randomVariable to this random variable.
    
    Parameters:
    
    randomVariable - A random variable (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - mult
```
RandomVariableInterface mult(RandomVariableInterface randomVariable)
```
    Applies x → x*randomVariable to this random variable.
    
    Parameters:
    
    randomVariable - A random variable (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - div
```
RandomVariableInterface div(RandomVariableInterface randomVariable)
```
    Applies x → x/randomVariable to this random variable.
    
    Parameters:
    
    randomVariable - A random variable (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - cap
```
RandomVariableInterface cap(RandomVariableInterface cap)
```
    Applies x → min(x,cap) to this random variable.
    
    Parameters:
    
    cap - The cap. A random variable (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - floor
```
RandomVariableInterface floor(RandomVariableInterface floor)
```
    Applies x → max(x,floor) to this random variable.
    
    Parameters:
    
    floor - The floor. A random variable (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - accrue
```
RandomVariableInterface accrue(RandomVariableInterface rate,
                               double periodLength)
```
    Applies x → x * (1.0 + rate * periodLength) to this random variable.
    
    Parameters:
    
    rate - The accruing rate. A random variable (compatible with this random variable).
    
    periodLength - The period length
    
    Returns:
    
    New random variable with the result of the function.
  - discount
```
RandomVariableInterface discount(RandomVariableInterface rate,
                                 double periodLength)
```
    Applies x → x / (1.0 + rate * periodLength) to this random variable.
    
    Parameters:
    
    rate - The discounting rate. A random variable (compatible with this random variable).
    
    periodLength - The period length
    
    Returns:
    
    New random variable with the result of the function.
  - barrier
```
RandomVariableInterface barrier(RandomVariableInterface trigger,
                                RandomVariableInterface valueIfTriggerNonNegative,
                                RandomVariableInterface valueIfTriggerNegative)
```
    Applies x → (trigger ≥ 0 ? valueIfTriggerNonNegative : valueIfTriggerNegative)
    
    Parameters:
    
    trigger - The trigger. A random variable (compatible with this random variable).
    
    valueIfTriggerNonNegative - The value used if the trigger is greater or equal 0
    
    valueIfTriggerNegative - The value used if the trigger is less than 0
    
    Returns:
    
    New random variable with the result of the function.
  - barrier
```
RandomVariableInterface barrier(RandomVariableInterface trigger,
                                RandomVariableInterface valueIfTriggerNonNegative,
                                double valueIfTriggerNegative)
```
    Applies x → (trigger ≥ 0 ? valueIfTriggerNonNegative : valueIfTriggerNegative)
    
    Parameters:
    
    trigger - The trigger. A random variable (compatible with this random variable).
    
    valueIfTriggerNonNegative - The value used if the trigger is greater or equal 0
    
    valueIfTriggerNegative - The value used if the trigger is less than 0
    
    Returns:
    
    New random variable with the result of the function.
  - invert
```
RandomVariableInterface invert()
```
    Applies x → 1/x to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - abs
```
RandomVariableInterface abs()
```
    Applies x → Math.abs(x), i.e. x → |x| to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - addProduct
```
RandomVariableInterface addProduct(RandomVariableInterface factor1,
                                   double factor2)
```
    Applies x → x + factor1 * factor2
    
    Parameters:
    
    factor1 - The factor 1. A random variable (compatible with this random variable).
    
    factor2 - The factor 2.
    
    Returns:
    
    New random variable with the result of the function.
  - addProduct
```
RandomVariableInterface addProduct(RandomVariableInterface factor1,
                                   RandomVariableInterface factor2)
```
    Applies x → x + factor1 * factor2
    
    Parameters:
    
    factor1 - The factor 1. A random variable (compatible with this random variable).
    
    factor2 - The factor 2. A random variable (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - addRatio
```
RandomVariableInterface addRatio(RandomVariableInterface numerator,
                                 RandomVariableInterface denominator)
```
    Applies x → x + numerator / denominator
    
    Parameters:
    
    numerator - The numerator of the ratio to add. A random variable (compatible with this random variable).
    
    denominator - The denominator of the ratio to add. A random variable (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - subRatio
```
RandomVariableInterface subRatio(RandomVariableInterface numerator,
                                 RandomVariableInterface denominator)
```
    Applies x → x - numerator / denominator
    
    Parameters:
    
    numerator - The numerator of the ratio to sub. A random variable (compatible with this random variable).
    
    denominator - The denominator of the ratio to sub. A random variable (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - isNaN
```
RandomVariableInterface isNaN()
```
    Applies x → (Double.isNaN(x) ? 1.0 : 0.0)
    
    Returns:
    
    A random variable which is 1.0 for all states that are NaN.
  - getMutableCopy
```
@Deprecated
RandomVariableInterface getMutableCopy()
```
    Deprecated.

Interface RandomVariableInterface

Method Summary

Method Detail

equals

getFiltrationTime

get

size

isDeterministic

getRealizations

getRealizations

getMin

getMax

getAverage

getAverage

getVariance

getVariance

getStandardDeviation

getStandardDeviation

getStandardError

getStandardError

getQuantile

getQuantile

getQuantileExpectation

getHistogram

getHistogram

cache

apply

cap

floor

add

sub

mult

div

pow

squared

sqrt

exp

log

sin

cos

add

sub

mult

div

cap

floor

accrue

discount

barrier

barrier

invert

abs

addProduct

addProduct

addRatio

subRatio

isNaN

getMutableCopy