Statistics Functions

Modules
	Absolute Maximum
	Computes the maximum value of absolute values of an array of data. The function returns both the maximum value and its position within the array. There are separate functions for floating-point, Q31, Q15, and Q7 data types.
	Absolute Minimum
	Computes the minimum value of absolute values of an array of data. The function returns both the minimum value and its position within the array. There are separate functions for floating-point, Q31, Q15, and Q7 data types.
	Accumulation functions
	Calculates the accumulation of the input vector. Sum is defined as the addition of the elements in the vector. The underlying algorithm is used:
	Entropy
	Computes the entropy of a distribution.
	Kullback-Leibler divergence
	Computes the Kullback-Leibler divergence between two distributions.
	LogSumExp
	LogSumExp optimizations to compute sum of probabilities with Gaussian distributions.
	Maximum
	Computes the maximum value of an array of data. The function returns both the maximum value and its position within the array. There are separate functions for floating-point, Q31, Q15, and Q7 data types.
	Mean
	Calculates the mean of the input vector. Mean is defined as the average of the elements in the vector. The underlying algorithm is used:
	Minimum
	Computes the minimum value of an array of data. The function returns both the minimum value and its position within the array. There are separate functions for floating-point, Q31, Q15, and Q7 data types.
	Mean Square Error
	Calculates the mean square error between two vectors.
	Power
	Calculates the sum of the squares of the elements in the input vector. The underlying algorithm is used:
	Root mean square (RMS)
	Calculates the Root Mean Square of the elements in the input vector. The underlying algorithm is used:
	Standard deviation
	Calculates the standard deviation of the elements in the input vector.
	Variance
	Calculates the variance of the elements in the input vector. The underlying algorithm used is the direct method sometimes referred to as the two-pass method:

Detailed Description