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A variable symbol overall is bound if at least one occurrence of it is bound. [ 1 ] pp.142--143 Since the same variable symbol may appear in multiple places in an expression, some occurrences of the variable symbol may be free while others are bound, [ 1 ] p.78 hence "free" and "bound" are at first defined for occurrences and then generalized ...
One method seeks to obtain analytical bounds which are inherently dependent on distribution parameters, and hence difficult to estimate. Another approach focuses on class densities, while yet another method combines and compares various classifiers.
The Cramér–Rao bound is stated in this section for several increasingly general cases, beginning with the case in which the parameter is a scalar and its estimator is unbiased. All versions of the bound require certain regularity conditions, which hold for most well-behaved distributions. These conditions are listed later in this section.
The Chernoff bound is exact if and only if is a single concentrated mass (degenerate distribution). The bound is tight only at or beyond the extremes of a bounded random variable, where the infima are attained for infinite . For unbounded random variables the bound is nowhere tight, though it is asymptotically tight up to sub-exponential ...
The following is the skeleton of a generic branch and bound algorithm for minimizing an arbitrary objective function f. [3] To obtain an actual algorithm from this, one requires a bounding function bound, that computes lower bounds of f on nodes of the search tree, as well as a problem-specific branching rule.
In probability theory, Cantelli's inequality (also called the Chebyshev-Cantelli inequality and the one-sided Chebyshev inequality) is an improved version of Chebyshev's inequality for one-sided tail bounds. [1] [2] [3] The inequality states that, for >,
Minkowski's bound may be used to derive a lower bound for the discriminant of a field K given n, r 1 and r 2. Since an integral ideal has norm at least one, we have 1 ≤ M K , so that | D | ≥ ( π 4 ) r 2 n n n ! ≥ ( π 4 ) n / 2 n n n !
As a result of its generality it may not (and usually does not) provide as sharp a bound as alternative methods that can be used if the distribution of the random variable is known. To improve the sharpness of the bounds provided by Chebyshev's inequality a number of methods have been developed; for a review see eg. [12] [37]