Titlebook: Computing Statistics under Interval and Fuzzy Uncertainty; Applications to Comp Hung T. Nguyen,Vladik Kreinovich,Gang Xiang Book 20121st ed

characteristic

Transfusion

Types of Interval Data Sets: Towards Feasible Algorithmsharacteristics are mean and variance. We already know that computing the mean under interval uncertainty is straightforward. However, as the previous chapter shows, computing variance . under interval uncertainty is, in general, an NP-hard (computationally difficult) problem. As we will see in the f

館長(zhǎng)

Computing Variance under Interval Uncertainty: Efficient Algorithmsnt . can be always computed in feasible (polynomial) time). Since we cannot always efficiently compute the upper endpoint . , we therefore need to consider cases when such an efficient computation may be possible.

Fierce

Computing Variance under Hierarchical Privacy-Related Interval Uncertaintye desired statistical characteristics..To prevent privacy violations, we replace the original values of the quasiidentifier variables with ranges. For example, we divide the set of all possible ages into ranges [0, 10], [10, 20], [20, 30], etc. Then, instead of storing the actual age of 26, we only