نتایج جستجو برای: Convergence in probability
تعداد نتایج: 17015678 فیلتر نتایج به سال:
the main objective in sampling is to select a sample from a population in order to estimate some unknown population parameter, usually a total or a mean of some interesting variable. a simple way to take a sample of size n is to let all the possible samples have the same probability of being selected. this is called simple random sampling and then all units have the same probability of being ch...
heuristics are often used to provide solutions for flow shop scheduling problems.the performance of a heuristic is usually judged by comparing solutions and run times on test cases.this investigation proposes an analytical alternative ,called asymptotic convergence ,which tests the convergence of the heuristic to a lower bound as problem size grows. the test is a stronger variation of worst cas...
let h be a separable hilbert space and let b be the set of bessel sequences in h. by using several interesting results in operator theory we study some topological properties of frames and riesz bases by constructing a banach space structure on b. the convergence of a sequence of elements in b is de_ned and we determine whether important properties of the sequence is preserved under the con...
abstract because of the many geopolitical, geo economical and geo strategically potentials and communicational capabilities of eco region, members can expand the convergence and the integration in base of this organization that have important impact on members development and expanding peace in international and regional level. based on quality analyzing of library findings and experts interv...
if $g$ is a connected graph with vertex set $v$, then the eccentric connectivity index of $g$, $xi^c(g)$, is defined as $sum_{vin v(g)}deg(v)ecc(v)$ where $deg(v)$ is the degree of a vertex $v$ and $ecc(v)$ is its eccentricity. in this paper we show some convergence in probability and an asymptotic normality based on this index in random bucket recursive trees.
in a detection network, the final decision is made by fusing the decisions from local detectors. the objective of that decision is to minimize the final error probability. to implement and optimal fusion rule, the performance of each detector, i.e. its probability of false alarm and its probability of missed detection as well as the a priori probabilities of the hypotheses, must be known. howev...
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