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Java library implementing Allen's Interval Algebra (incl. data and index structures, e.g., interval tree) OWL-Time Time Ontology in OWL an OWL-2 DL ontology of temporal concepts, for describing the temporal properties of resources in the world or described in Web pages. GQR is a reasoner for Allen's interval algebra (and many others)
The probability density function (PDF) for the Wilson score interval, plus PDF s at interval bounds. Tail areas are equal. Since the interval is derived by solving from the normal approximation to the binomial, the Wilson score interval ( , + ) has the property of being guaranteed to obtain the same result as the equivalent z-test or chi-squared test.
If only a fixed number of pairwise comparisons are to be made, the Tukey–Kramer method will result in a more precise confidence interval. In the general case when many or all contrasts might be of interest, the Scheffé method is more appropriate and will give narrower confidence intervals in the case of a large number of comparisons.
Any factors for roots outside the interval from a to b will not change sign over that interval. Finally, for factors corresponding to roots x i inside the interval from a to b that are of odd multiplicity, multiply p n by one more factor to make a new polynomial p n ( x ) ∏ i ( x − x i ) . {\displaystyle p_{n}(x)\,\prod _{i}(x-x_{i}).}
For some applications, the integration interval = [,] needs to be divided into uneven intervals – perhaps due to uneven sampling of data, or missing or corrupted data points. Suppose we divide the interval I {\\displaystyle I} into an even number N {\\displaystyle N} of subintervals of widths h k {\\displaystyle h_{k}} .
The golden-section search is a technique for finding an extremum (minimum or maximum) of a function inside a specified interval. For a strictly unimodal function with an extremum inside the interval, it will find that extremum, while for an interval containing multiple extrema (possibly including the interval boundaries), it will converge to one of them.
This yields 1.15448, which is not in the interval between (3a 3 + b 3) / 4 and b 3). Hence, it is replaced by the midpoint m = −2.71449. We have f(m) = 3.93934, so we set a 4 = a 3 and b 4 = −2.71449. In the fifth iteration, inverse quadratic interpolation yields −3.45500, which lies in the required interval.
Interval scheduling is a class of problems in computer science, particularly in the area of algorithm design. The problems consider a set of tasks. Each task is represented by an interval describing the time in which it needs to be processed by some machine (or, equivalently, scheduled on some resource). For instance, task A might run from 2:00 ...