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To use these functions, the Probabilistic Feature is required.
The probabilistic feature provides arbitrary functions to work with disc= rete and continuous random variables in a data stream and provides algebrai= c operator (+, *, -, /) to perform probabilistic addition, subtraction, mul= tiplication, division, and exponentiation.
Estimates the multivariate normal distribution probability with lower an= d upper integration limit.
Converts the two object into a 2D vector.
Similar to the as2DVector function, this function creates a 3D vector wi= th the given objects.
Calculates the Bhattacharyya distance= between two distributions.
SELECT = similarity(as2DVector(x1,y1), as2DVector(x2,y2)) FROM stream
Calculates the Mahalanobis distance bet= ween the distribution and the value. The value can be a scalar value or a v= ector.
SELECT = distance(as3DVector(x, y, z), [1.0;2.0;3.0]) FROM stream
Calculates the Kullback-Leib= ler divergence of the two given probability distributions.
SELECT = kl(as3DVector(x, y, z), as3DVector(a, b, c)) FROM stream
Calculates the log Likelihood between the given points and the prob= ability distribution.
SELECT = loglikelihood([1.0;2.0;3.0], x) FROM stream