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35
Multicasting in the Hypercube, Chord and Binomial Graphs
, 2009
"... We discuss multicasting for the ncube network and its close variants, the Chord and the Binomial Graph (BNG) Network. We present simple transformations and proofs that establish that the spmulticast (shortest path) and Steiner tree problems for the ncube, Chord and the BNG network are NPComplete ..."
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We discuss multicasting for the ncube network and its close variants, the Chord and the Binomial Graph (BNG) Network. We present simple transformations and proofs that establish that the spmulticast (shortest path) and Steiner tree problems for the ncube, Chord and the BNG network are NP
[NP3546/20A]
"... Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementationdependent details. 1 Purpose G05MJF generates a vector of pseudorandom 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 implementationdependent details. 1 Purpose G05MJF generates a vector of pseudorandom integers from the discrete binomial distribution
[NP3546/20A]
"... Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementationdependent details. 1 Purpose G05MCF generates a vector of pseudorandom 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 implementationdependent details. 1 Purpose G05MCF generates a vector of pseudorandom integers from the discrete negative binomial distribution
[NP3666/22]
"... Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementationdependent 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 implementationdependent details. 1 Purpose G05MCF generates a vector of pseudorandom integers from the discrete negative binomial distribution
The AllOnes Problem for Binomial Trees, Butterfly and Benes Networks
"... The allones problem is an NPcomplete problem introduced by Sutner [11], with wide applications in linear cellular automata. In this paper, we solve the allones problem for some of the widely studied architectures like binomial trees, butterfly, and benes networks. ..."
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The allones problem is an NPcomplete problem introduced by Sutner [11], with wide applications in linear cellular automata. In this paper, we solve the allones problem for some of the widely studied architectures like binomial trees, butterfly, and benes networks.
SOME COMBINATORICS OF BINOMIAL COEFFICIENTS AND THE BLOCHGIESEKER PROPERTY FOR SOME HOMOGENEOUS BUNDLES
, 2001
"... Abstract. A vector bundle has the BlochGieseker property if all its Chern classes are numerically positive. In this paper we show that the nonample bundle pPn (p+ 1) has the BlochGieseker 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 coecient 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
"... This document provides proofs of the following: • The binomial distribution can be approximated with a Gaussian distribution at large values of N. • The arcsin squareroot 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 (nN) = 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
, 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
 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 ..."
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Cited by 26 (16 self)
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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
"... 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
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35