RandomVariable (finmath lib 4.1.1 API)

All Superinterfaces:

Serializable

All Known Subinterfaces:

RandomVariableAccumulator, RandomVariableArray, RandomVariableDifferentiable

All Known Implementing Classes:

RandomVariableArrayImplementation, RandomVariableDifferentiableAAD, RandomVariableDifferentiableAD, RandomVariableFromDoubleArray, RandomVariableFromFloatArray, RandomVariableLazyEvaluation, Scalar
```
public interface RandomVariable
extends Serializable
```
This interface describes the methods implemented by an immutable random variable. The random variable is immutable, i.e. method calls like add, sub, mult will return a new instance and leave the method receiver random variable unchanged (immutable). This is used to ensure that arguments or return values are not changed.

Version:

1.5

Author:

Christian Fries

Method Summary

All Methods Instance Methods Abstract Methods Default Methods
Modifier and Type	Method and Description
`RandomVariable`	`abs()` Applies x → Math.abs(x), i.e. x → \|x\| to this random variable.
`RandomVariable`	`accrue(RandomVariable rate, double periodLength)` Applies x → x * (1.0 + rate * periodLength) to this random variable.
`RandomVariable`	`add(double value)` Applies x → x + value to this random variable.
`RandomVariable`	`add(RandomVariable randomVariable)` Applies x → x+randomVariable to this random variable.
`RandomVariable`	`addProduct(RandomVariable factor1, double factor2)` Applies x → x + factor1 * factor2
`RandomVariable`	`addProduct(RandomVariable factor1, RandomVariable factor2)` Applies x → x + factor1 * factor2
`RandomVariable`	`addRatio(RandomVariable numerator, RandomVariable denominator)` Applies x → x + numerator / denominator
`default RandomVariable`	`addSumProduct(List<RandomVariable> factor1, List<RandomVariable> factor2)` Applies \( x \mapsto x + \sum_{i=0}^{n-1} factor1_{i} * factor2_{i}
`default RandomVariable`	`addSumProduct(RandomVariable[] factor1, RandomVariable[] factor2)` Applies \( x \mapsto x + \sum_{i=0}^{n-1} factor1_{i} * factor2_{i}
`RandomVariable`	`apply(DoubleBinaryOperator operator, RandomVariable argument)` Applies x → operator(x,y) to this random variable, where x is this random variable and y is a given random variable.
`RandomVariable`	`apply(DoubleTernaryOperator operator, RandomVariable argument1, RandomVariable argument2)` Applies x → operator(x,y,z) to this random variable, where x is this random variable and y and z are given random variable.
`RandomVariable`	`apply(DoubleUnaryOperator operator)` Applies x → operator(x) to this random variable.
`RandomVariable`	`average()` Returns a random variable which is deterministic and corresponds the expectation of this random variable.
`RandomVariable`	`bus(RandomVariable randomVariable)` Applies x → randomVariable-x to this random variable.
`RandomVariable`	`cache()` Return a cacheable version of this object (often a self-reference).
`RandomVariable`	`cap(double cap)` Applies x → min(x,cap) to this random variable.
`RandomVariable`	`cap(RandomVariable cap)` Applies x → min(x,cap) to this random variable.
`RandomVariable`	`choose(RandomVariable valueIfTriggerNonNegative, RandomVariable valueIfTriggerNegative)` Applies x → (x ≥ 0 ?
`RandomVariable`	`cos()` Applies x → cos(x) to this random variable.
`RandomVariable`	`discount(RandomVariable rate, double periodLength)` Applies x → x / (1.0 + rate * periodLength) to this random variable.
`RandomVariable`	`div(double value)` Applies x → x / value to this random variable.
`RandomVariable`	`div(RandomVariable randomVariable)` Applies x → x/randomVariable to this random variable.
`Double`	`doubleValue()` Returns the double value if isDeterministic() is true. otherwise throws an `UnsupportedOperationException`.
`boolean`	`equals(RandomVariable randomVariable)` Compare this random variable with a given one
`RandomVariable`	`exp()` Applies x → exp(x) to this random variable.
`default RandomVariable`	`expm1()` Applies x → expm1(x) (that is x → exp(x)-1.0) to this random variable.
`RandomVariable`	`floor(double floor)` Applies x → max(x,floor) to this random variable.
`RandomVariable`	`floor(RandomVariable 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(RandomVariable probabilities)` Returns the expectation of this random variable for a given probability measure (weight).
`default RandomVariable`	`getConditionalExpectation(ConditionalExpectationEstimator conditionalExpectationOperator)` Returns the conditional expectation using a given conditional expectation estimator.
`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.
`IntToDoubleFunction`	`getOperator()` Returns the operator path → this.get(path) corresponding to this random variable.
`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, RandomVariable 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.
`DoubleStream`	`getRealizationsStream()` Returns a stream of doubles corresponding to the realizations of this random variable.
`double`	`getSampleVariance()` Returns the sample variance of this random variable, i.e., V * size()/(size()-1) where V = getVariance().
`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(RandomVariable 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(RandomVariable probabilities)` Returns the standard error (discretization error) of this random variable.
`int`	`getTypePriority()` Returns the type priority.
`default RandomVariable`	`getValues()` Returns the underlying values and a 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(RandomVariable 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).
`RandomVariable`	`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.
`RandomVariable`	`isNaN()` Applies x → (Double.isNaN(x) ?
`RandomVariable`	`log()` Applies x → log(x) to this random variable.
`RandomVariable`	`mult(double value)` Applies x → x * value to this random variable.
`RandomVariable`	`mult(RandomVariable randomVariable)` Applies x → x*randomVariable to this random variable.
`RandomVariable`	`pow(double exponent)` Applies x → pow(x,exponent) to this random variable.
`RandomVariable`	`sin()` Applies x → sin(x) to this random variable.
`int`	`size()` Returns the number of paths or states.
`RandomVariable`	`sqrt()` Applies x → sqrt(x) to this random variable.
`RandomVariable`	`squared()` Applies x → x * x to this random variable.
`RandomVariable`	`sub(double value)` Applies x → x - value to this random variable.
`RandomVariable`	`sub(RandomVariable randomVariable)` Applies x → x-randomVariable to this random variable.
`RandomVariable`	`subRatio(RandomVariable numerator, RandomVariable denominator)` Applies x → x - numerator / denominator
`RandomVariable`	`vid(RandomVariable randomVariable)` Applies x → randomVariable/x to this random variable.

- Method Detail
  - equals
```
boolean equals(RandomVariable 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.
  - getTypePriority
```
int getTypePriority()
```
    Returns the type priority.
    
    Returns:
    
    The type priority.
    
    See Also:
    
    ssrn abstract 3246127
  - 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.
  - getValues
```
default RandomVariable getValues()
```
    Returns the underlying values and a random variable. If the implementation supports an "inner representation", returns the inner representation. Otherwise just returns this.
    
    Returns:
    
    The underling values.
  - 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.
  - doubleValue
```
Double doubleValue()
```
    Returns the double value if isDeterministic() is true. otherwise throws an UnsupportedOperationException.
    
    Returns:
    
    The double value if isDeterministic() is true, otherwise throws an an UnsupportedOperationException.
  - getOperator
```
IntToDoubleFunction getOperator()
```
    Returns the operator path → this.get(path) corresponding to this random variable.
    
    Returns:
    
    The operator path → this.get(path) corresponding to this random variable.
  - getRealizationsStream
```
DoubleStream getRealizationsStream()
```
    Returns a stream of doubles corresponding to the realizations of this random variable.
    
    Returns:
    
    A stream of doubles corresponding to the realizations of this random variable.
  - 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. The result of this method has to agrees with average().doubleValue().
    
    Returns:
    
    The average assuming equi-distribution.
  - getAverage
```
double getAverage(RandomVariable 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(RandomVariable 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.
  - getSampleVariance
```
double getSampleVariance()
```
    Returns the sample variance of this random variable, i.e., V * size()/(size()-1) where V = getVariance().
    
    Returns:
    
    The sample variance.
  - 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(RandomVariable 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(RandomVariable probabilities)
```
    Returns the standard error (discretization error) of this random variable. For a Monte-Carlo simulation this is 1/Math.sqrt(n) * getStandardDeviation(RandomVariable).
    
    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) wiil return the smallest value and getQuantile(1) will return the largest value.
    
    Parameters:
    
    quantile - The quantile level.
    
    Returns:
    
    The quantile value assuming equi-distribution.
  - getQuantile
```
double getQuantile(double quantile,
                   RandomVariable 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
```
RandomVariable 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
```
RandomVariable apply(DoubleUnaryOperator operator)
```
    Applies x → operator(x) to this random variable. It returns a new random variable with the result.
    
    Parameters:
    
    operator - An unary operator/function, mapping double to double.
    
    Returns:
    
    New random variable with the result of the function.
  - apply
```
RandomVariable apply(DoubleBinaryOperator operator,
                     RandomVariable argument)
```
    Applies x → operator(x,y) to this random variable, where x is this random variable and y is a given random variable. It returns a new random variable with the result.
    
    Parameters:
    
    operator - A binary operator/function, mapping (double,double) to double.
    
    argument - A random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - apply
```
RandomVariable apply(DoubleTernaryOperator operator,
                     RandomVariable argument1,
                     RandomVariable argument2)
```
    Applies x → operator(x,y,z) to this random variable, where x is this random variable and y and z are given random variable. It returns a new random variable with the result.
    
    Parameters:
    
    operator - A ternary operator/function, mapping (double,double,double) to double.
    
    argument1 - A random variable representing y.
    
    argument2 - A random variable representing z.
    
    Returns:
    
    New random variable with the result of the function.
  - cap
```
RandomVariable 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
```
RandomVariable 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
```
RandomVariable 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
```
RandomVariable 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
```
RandomVariable 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
```
RandomVariable 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
```
RandomVariable 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.
  - average
```
RandomVariable average()
```
    Returns a random variable which is deterministic and corresponds the expectation of this random variable.
    
    Returns:
    
    New random variable being the expectation of this random variable.
  - getConditionalExpectation
```
default RandomVariable getConditionalExpectation(ConditionalExpectationEstimator conditionalExpectationOperator)
```
    Returns the conditional expectation using a given conditional expectation estimator.
    
    Parameters:
    
    conditionalExpectationOperator - A given conditional expectation estimator.
    
    Returns:
    
    The conditional expectation of this random variable (as a random variable)
  - squared
```
RandomVariable squared()
```
    Applies x → x * x to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - sqrt
```
RandomVariable sqrt()
```
    Applies x → sqrt(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - exp
```
RandomVariable exp()
```
    Applies x → exp(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - expm1
```
default RandomVariable expm1()
```
    Applies x → expm1(x) (that is x → exp(x)-1.0) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - log
```
RandomVariable log()
```
    Applies x → log(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - sin
```
RandomVariable sin()
```
    Applies x → sin(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - cos
```
RandomVariable cos()
```
    Applies x → cos(x) to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - add
```
RandomVariable add(RandomVariable 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
```
RandomVariable sub(RandomVariable 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.
  - bus
```
RandomVariable bus(RandomVariable randomVariable)
```
    Applies x → randomVariable-x 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
```
RandomVariable mult(RandomVariable 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
```
RandomVariable div(RandomVariable 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.
  - vid
```
RandomVariable vid(RandomVariable randomVariable)
```
    Applies x → randomVariable/x 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
```
RandomVariable cap(RandomVariable 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
```
RandomVariable floor(RandomVariable 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
```
RandomVariable accrue(RandomVariable 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
```
RandomVariable discount(RandomVariable 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.
  - choose
```
RandomVariable choose(RandomVariable valueIfTriggerNonNegative,
                      RandomVariable valueIfTriggerNegative)
```
    Applies x → (x ≥ 0 ? valueIfTriggerNonNegative : valueIfTriggerNegative)
    
    Parameters:
    
    valueIfTriggerNonNegative - The value used if this is greater or equal 0
    
    valueIfTriggerNegative - The value used if the this is less than 0
    
    Returns:
    
    New random variable with the result of the function.
  - invert
```
RandomVariable invert()
```
    Applies x → 1/x to this random variable.
    
    Returns:
    
    New random variable with the result of the function.
  - abs
```
RandomVariable 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
```
RandomVariable addProduct(RandomVariable 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
```
RandomVariable addProduct(RandomVariable factor1,
                          RandomVariable 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
```
RandomVariable addRatio(RandomVariable numerator,
                        RandomVariable 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
```
RandomVariable subRatio(RandomVariable numerator,
                        RandomVariable 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.
  - addSumProduct
```
default RandomVariable addSumProduct(RandomVariable[] factor1,
                                     RandomVariable[] factor2)
```
    Applies \( x \mapsto x + \sum_{i=0}^{n-1} factor1_{i} * factor2_{i}
    
    Parameters:
    
    factor1 - The factor 1. A list of random variables (compatible with this random variable).
    
    factor2 - The factor 2. A list of random variables (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - addSumProduct
```
default RandomVariable addSumProduct(List<RandomVariable> factor1,
                                     List<RandomVariable> factor2)
```
    Applies \( x \mapsto x + \sum_{i=0}^{n-1} factor1_{i} * factor2_{i}
    
    Parameters:
    
    factor1 - The factor 1. A list of random variables (compatible with this random variable).
    
    factor2 - The factor 2. A list of random variables (compatible with this random variable).
    
    Returns:
    
    New random variable with the result of the function.
  - isNaN
```
RandomVariable isNaN()
```
    Applies x → (Double.isNaN(x) ? 1.0 : 0.0)
    
    Returns:
    
    A random variable which is 1.0 for all states that are NaN, otherwise 0.0.

Interface RandomVariable

Method Summary

Method Detail

equals

getFiltrationTime

getTypePriority

get

size

isDeterministic

getValues

getRealizations

doubleValue

getOperator

getRealizationsStream

getMin

getMax

getAverage

getAverage

getVariance

getVariance

getSampleVariance

getStandardDeviation

getStandardDeviation

getStandardError

getStandardError

getQuantile

getQuantile

getQuantileExpectation

getHistogram

getHistogram

cache

apply

apply

apply

cap

floor

add

sub

mult

div

pow

average

getConditionalExpectation

squared

sqrt

exp

expm1

log

sin

cos

add

sub

bus

mult

div

vid

cap

floor

accrue

discount

choose

invert

abs

addProduct

addProduct

addRatio

subRatio

addSumProduct

addSumProduct

isNaN