Search results
Results from the WOW.Com Content Network
The weighted arithmetic mean is similar to an ordinary arithmetic mean (the most common type of average), except that instead of each of the data points contributing equally to the final average, some data points contribute more than others.
The maximum likelihood method weights the difference between fit and data using the same weights . The expected value of a random variable is the weighted average of the possible values it might take on, with the weights being the respective probabilities. More generally, the expected value of a function of a random variable is the probability ...
Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. It is an easily learned ...
In statistics, inverse-variance weighting is a method of aggregating two or more random variables to minimize the variance of the weighted average. Each random variable is weighted in inverse proportion to its variance (i.e., proportional to its precision). Given a sequence of independent observations y i with variances σ i 2, the inverse ...
Note that the unweighted term indicates that all distances contribute equally to each average that is computed and does not refer to the math by which it is achieved. Thus the simple averaging in WPGMA produces a weighted result and the proportional averaging in UPGMA produces an unweighted result (see the working example). [2]
The values of these weights depend on the degrees of the two sets of nodes in the original bipartite network. For example, in a co-authorship network, [ 4 ] the number of observed co-authorships depends on (1) the number of papers each author wrote and (2) the number of authors on each paper.
inverse-variance weighting, also known as analytic weights, [24] is when each element is assigned a weight that is the inverse of its (known) variance. [25] [9]: 187 When all elements have the same expectancy, using such weights for calculating weighted averages has the least variance among all weighted averages. In the common formulation ...
The averaging procedure therefore gives differential weight to the initial distances of matrix . This is the reason why the method is weighted , not with respect to the mathematical procedure but with respect to the initial distances.