Internal state of the West & Chan running variance algorithm. Fields of this proto should not be accessed directly; instead, please use RunningMeanAndVarianceUtil. The fields of this message only have meaning in the context of the West & Chan algorithm, which is documented (or Wikipedia-linked) in the doc comments of RunningMeanAndVarianceUtil. We do however give some explanation of the meanings of these fields in the context of the algorithm (i.e. if you have the Wikipedia page open and are ready to do some math). Also see the file doc of RunningMeanAndVarianceUtil for a specification and more info about the algorithm. Notation: The data set is X = {(x_1, w_1), ..., (x_n, w_n)}. It consists of n weighted data points. The ith data point has value x_i and weight w_i. REQUIRES: x_i is finite for each i. w_i is finite for each i. w_i >= 0 for each i.

Attributes

• m2 (type: float(), default: nil) - The variable which in the Wikipedia page is referred to as M_2: m2 = w_1 (x_1 - mean)^2 + ... + w_n (x_n - mean)^2. The algorithm implemented in RunningMeanAndVarianceUtil provides a way to update m2 in a numerically stable way when the data set grows. If total_weight = 0, then m2 is meaningless, and its value is unspecified, except that it must be finite and >= 0.
• mean (type: float(), default: nil) - Mean of the data set, mean = (w_1 x_1 + ... + w_n x_n) / total_weight. The algorithm implemented in RunningMeanAndVarianceUtil provides a way to update this mean in a numerically stable way when the data set grows. If total_weight = 0, then mean is meaningless, and its value is unspecified, except that it must be finite.
• totalWeight (type: float(), default: nil) - Total weight of the data set, total_weight = w_1 + ... + w_n.