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There are many variations of the weighted majority algorithm to handle different situations, like shifting targets, infinite pools, or randomized predictions. The core mechanism remains similar, with the final performances of the compound algorithm bounded by a function of the performance of the specialist (best performing algorithm) in the pool.
An index that is weighted in this manner is said to be "float-adjusted" or "float-weighted", in addition to being cap-weighted. For example, the S&P 500 index is both cap-weighted and float-adjusted. [3] Historically, in the United States, capitalization-weighted indices tended to use full weighting, i.e., all outstanding shares were included ...
Given the same setup with N experts. Consider the special situation where the proportions of experts predicting positive and negative, counting the weights, are both close to 50%. Then, there might be a tie. Following the weight update rule in weighted majority algorithm, the predictions made by the algorithm would be randomized.
Hasse diagram of some classes of quantitative automata, ordered by expressiveness. [1]: Fig.1 In theoretical computer science and formal language theory, a weighted automaton or weighted finite-state machine is a generalization of a finite-state machine in which the edges have weights, for example real numbers or integers.
Let j in R be the index of max(w R) in w. Add j to P. Remove j from R. Let A P be A restricted to the variables included in P. Let s be vector of same length as x. Let s P denote the sub-vector with indexes from P, and let s R denote the sub-vector with indexes from R. Set s P = ((A P) T A P) −1 (A P) T y; Set s R to zero; While min(s P) ≤ 0:
Rendezvous or highest random weight (HRW) hashing [1] [2] is an algorithm that allows clients to achieve distributed agreement on a set of options out of a possible set of options. A typical application is when clients need to agree on which sites (or proxies) objects are assigned to.
That way, using the example of an S&P 500 index fund, each company would be .2% of the fund. ... Greenblatt's Choice: Value-Weighted Index Funds There is yet another way to structure an index -- a ...
This algorithm can easily be adapted to compute the variance of a finite population: simply divide by n instead of n − 1 on the last line.. Because SumSq and (Sum×Sum)/n can be very similar numbers, cancellation can lead to the precision of the result to be much less than the inherent precision of the floating-point arithmetic used to perform the computation.