Results 1 - 10 of 35

Multicasting in the Hypercube, Chord and Binomial Graphs

by Christopher C. Cipriano, Teofilo F. Gonzalez , 2009
"... We discuss multicasting for the n-cube network and its close variants, the Chord and the Binomial Graph (BNG) Network. We present simple transformations and proofs that establish that the sp-multicast (shortest path) and Steiner tree problems for the n-cube, Chord and the BNG network are NP-Complete ..."
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We discuss multicasting for the n-cube network and its close variants, the Chord and the Binomial Graph (BNG) Network. We present simple transformations and proofs that establish that the sp-multicast (shortest path) and Steiner tree problems for the n-cube, Chord and the BNG network are NP

[NP3546/20A]

by Gmjf. Gmjf
"... Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details. 1 Purpose G05MJF generates a vector of pseudo-random integers from the discrete binomial distribution with parameters ..."
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Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details. 1 Purpose G05MJF generates a vector of pseudo-random integers from the discrete binomial distribution

[NP3546/20A]

by Gmcf. Gmcf
"... Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details. 1 Purpose G05MCF generates a vector of pseudo-random integers from the discrete negative binomial distribution with p ..."
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Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details. 1 Purpose G05MCF generates a vector of pseudo-random integers from the discrete negative binomial distribution

[NP3666/22]

by Gmcf. Gmcf
"... Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details. 1 Purpose G05MCF generates a vector of pseudorandom integers from the discrete negative binomial distribution with pa ..."
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Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details. 1 Purpose G05MCF generates a vector of pseudorandom integers from the discrete negative binomial distribution

The All-Ones Problem for Binomial Trees, Butterfly and Benes Networks

by Paul Manuel, Indra Rajasingh, Bharathi Rajan, R. Prabha
"... The all-ones problem is an NP-complete problem introduced by Sutner [11], with wide applica-tions in linear cellular automata. In this paper, we solve the all-ones problem for some of the widely studied architectures like binomial trees, butterfly, and benes networks. ..."
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The all-ones problem is an NP-complete problem introduced by Sutner [11], with wide applica-tions in linear cellular automata. In this paper, we solve the all-ones problem for some of the widely studied architectures like binomial trees, butterfly, and benes networks.

SOME COMBINATORICS OF BINOMIAL COEFFICIENTS AND THE BLOCH-GIESEKER PROPERTY FOR SOME HOMOGENEOUS BUNDLES

by Mei-chu Chang , 2001
"... Abstract. A vector bundle has the Bloch-Gieseker property if all its Chern classes are numerically positive. In this paper we show that the non-ample bun-dle pPn (p+ 1) has the Bloch-Gieseker property, except for two cases, in which the top Chern classes are trivial and the other Chern classes are p ..."
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are positive. Our method is to reduce the problem to showing, e.g. the positivity of the coe-cient of tk in the rational function (1+t) (np)(1+3t)( n

The Effects of an Arcsin Square Root Transform on a Binomial Distributed Quantity

by P. A. Bromiley, N. A. Thacker, P. A. Bromiley, N. A. Thacker
"... This document provides proofs of the following: • The binomial distribution can be approximated with a Gaussian distribution at large values of N. • The arcsin square-root transform is the variance stabilising transform for the binomial distribution. • The Gaussian approximation for the binomial dis ..."
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and Poisson distributions. 1 Gaussian Approximation to the Binomial Distribution The binomial distribution P (n|N) = N! n!(N − n)!p nq(N−n) (1) gives the probability of obtaining n successes out of N Bernoulli trials, where p is the probability of success and q = 1 − p is the probability of failure

Abstract Multicast Session Membership Size Estimation

by Timur Friedman, Don Towsley , 1998
"... The problem of estimating the number of members in a multicast session through probabilistic polling corresponds to that of estimating the parameter n of the Binomial np, distribution. This allows an interval estimator for n to be derived. The tradeoff between the relative dispersion of this estimat ..."
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The problem of estimating the number of members in a multicast session through probabilistic polling corresponds to that of estimating the parameter n of the Binomial np, distribution. This allows an interval estimator for n to be derived. The tradeoff between the relative dispersion

An extension of Lucas’ theorem

by Hong Hu, Zhi-wei Sun, Communicated David E. Rohrlich - Proc. Amer. Math. Soc
"... Abstract. Let p be a prime. A famous theorem of Lucas states that ( mp+s) ≡ ( np+t m) ( s) (mod p) ifm, n, s, t are nonnegative integers with s, t < p. Inthispaper n t we aim to prove a similar result for generalized binomial coefficients defined in terms of second order recurrent sequences with ..."
Abstract - Cited by 26 (16 self) - Add to MetaCart
Abstract. Let p be a prime. A famous theorem of Lucas states that ( mp+s) ≡ ( np+t m) ( s) (mod p) ifm, n, s, t are nonnegative integers with s, t < p. Inthispaper n t we aim to prove a similar result for generalized binomial coefficients defined in terms of second order recurrent sequences

18.443 Statistics for Applications

by Mit Opencourseware, R. M. Dudley
"... First, here is some notation for binomial probabilities. Let X be the number of successes in n independent trials with probability p of success on each trial. Let q ≡ 1−p. Then we know that EX = np, the variance of X is npq where q = 1 − p, and so the ..."
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First, here is some notation for binomial probabilities. Let X be the number of successes in n independent trials with probability p of success on each trial. Let q ≡ 1−p. Then we know that EX = np, the variance of X is npq where q = 1 − p, and so the
Results 1 - 10 of 35