To use these functions, the Probabilistic Feature is required.
The probabilistic feature provides arbitrary functions to work with discrete and continuous random variables in a data stream and provides algebraic operator (+, *, -, /) to perform probabilistic addition, subtraction, multiplication, division, and exponentiation.
Int(<ProbabilisticDouble x, Number a, Number b>|<Vector xyz, Vector a, Vector b>)
Estimates the multivariate normal distribution probability with lower and upper integration limit.
as2DVector(ProbabilisticDouble x, ProbabilisticDouble y)
Converts the two object into a 2D vector.
as3DVector(ProbabilisticDouble x, ProbabilisticDouble y, ProbabilisticDouble z)
Similar to the as2DVector function, this function creates a 3D vector with the given objects.
Similarity(ProbabilisticDouble p1, ProbabilisticDouble p2)
Calculates the Bhattacharyya distance between two distributions.
Distance(<ProbabilisticDouble p, Number x>|<Vector p, Vector x>)
Calculates the Mahalanobis distance between the distribution and the value. The value can be a scalar value or a vector.
KL(<ProbabilisticDouble p1, ProbabilisticDouble p2>|<Vector p1, Vector p2>)
Calculates the Kullback-Leibler divergence of the two given probability distributions.
LogLikelihood(Vector points, ProbabilisticDouble p)
Calculates the log Likelihood between the given points and the probability distribution.