Topic
Probability-generating function
About: Probability-generating function is a research topic. Over the lifetime, 752 publications have been published within this topic receiving 9361 citations.
Papers published on a yearly basis
Papers
More filters
••
30 Jun 2002
TL;DR: It is shown that any variable-length URNG in the class is asymptotically optimal for any given general source, and the output length of such URNG per source symbol converges in probability to the self-information of the source per source symbols.
Abstract: We propose a new class of variable-length uniform random number generators (URNGs) and investigate their asymptotic properties. It is shown that (i) any variable-length URNG in the class is asymptotically optimal for any given general source, and (ii) the output length of such URNG per source symbol converges in probability to the self-information of the source per source symbol.
••
TL;DR: In this paper, a general procedure to calculate the probability density function of a function of random variable is presented, which has been successfully used at the undergraduate level for several semesters at the Pontificia Universidade Catolica do Rio de Janeiro.
Abstract: A general procedure to calculate the probability density function of a function of a random variable is presented. Although it is not really a new method of solving this problem, it is a procedure which is organized and developed in a manner better suited to student understanding and application than other methods. This technique has been successfully used at the undergraduate level for several semesters at the Pontificia Universidade Catolica do Rio de Janeiro.
••
06 Sep 1993TL;DR: Tight upper and lower bounds are obtained, which together provide very accurate estimates of the actual error probability, and some applications are presented to show their general applicability and to illustrate their tightness.
Abstract: The problem of efficiently evaluating the probability that the magnitude squared of one complex Gaussian random variable is less than the magnitude squared of another, possibly correlated, complex Gaussian random variable is addressed. This is known as an error probability of the Rician-type. In this paper, a bounding approach is taken. Tight upper and lower bounds are obtained, which together provide very accurate estimates of the actual error probability. Some applications of the bounds are presented to show their general applicability and to illustrate their tightness.
••
TL;DR: In this paper, the authors developed some results presented by Gani (2004), deriving moments for random allocation processes These moments correspond to the allocation processes reaching some domain boundary Exact formulae for means, variances, and probability generating functions as well as some asymptotic formulas for moments of random allocation process are obtained.
Abstract: In this paper we develop some results presented by Gani (2004), deriving moments for random allocation processes These moments correspond to the allocation processes reaching some domain boundary Exact formulae for means, variances, and probability generating functions as well as some asymptotic formulae for moments of random allocation processes are obtained A special choice of the asymptotics and of the domain allows us to reduce a complicated numerical procedure to a simple asymptotic one
••
18 Oct 2008TL;DR: It is proven that characteristic function for random fuzzy variable phi(t 1, t2) is differentiable in t and t, respectively, and partial derivatives of phi (t1, t2) are continuous.
Abstract: A new concept of characteristic function for random fuzzy variable is introduced and some properties of characteristic function are obtained. It is proven that characteristic function for random fuzzy variable phi(t1, t2) is differentiable in t1 and t2, respectively, and partial derivatives of phi(t1,t2) are continuous. Furthermore, the Taylor's expansion of phi(t1,t2) is given and the relation between chancedistribution and characteristic function such as inversion formulais obtained.