In this paper, another tail-end probability function is proposed using the left tail-end probabilities, p( ≤ i ) = Πṙ The resulting function, πx(t), is continuous and converges uniformly within the unit circle, | t | < 1. A clear functional link is established between πx(t) and two other well known versions of the probability generating function. When known, πx(t) uniquely generates the components of the probability mass function of the discrete random variable, and indirectly generates moments.Keywords: Probability Generating Function, Tail – end Probabilities, Convergence, Moments
Two deficiencies in using moment-generating functions are given and illustrated with examples. Many ...
The canonical way to establish the central limit theorem for i.i.d. random variables is to use chara...
summary:Part II of the paper aims at providing conditions which may serve as a bridge between existi...
In this paper, we describe a tool to aid in proving theorems about random variables, called the mome...
Generating functions are suitable mathematical apparatus to describe the distribution of random vari...
Since histograms give little quantitative information about distribution, more detail descriptions a...
Characteristic Functions (cf) have been used to establish the convergence of several independent and...
Includes bibliographical references.This paper is concerned with a particularly useful function of p...
In this note, we show that if a sequence of moment generating functions Mn(t) converges pointwise to...
The probability mass function of a pair of discrete random variables (X,Y) is the function f(x,y)=P(...
The theory of Fourier transforms of generalized functions is used to extract general formulas for th...
45 pages, 1 article*Computing Tail Probabilities by Numerical Fourier Inversion: the Absolutely Con...
This thesis will focus on generating the probability mass function using Fibonacci sequenceas the co...
This book contains a systematic treatment of probability from the ground up, starting with intuitive...
These Lecture Slide Notes have been used for a two-quarter graduate level sequence in probability co...
Two deficiencies in using moment-generating functions are given and illustrated with examples. Many ...
The canonical way to establish the central limit theorem for i.i.d. random variables is to use chara...
summary:Part II of the paper aims at providing conditions which may serve as a bridge between existi...
In this paper, we describe a tool to aid in proving theorems about random variables, called the mome...
Generating functions are suitable mathematical apparatus to describe the distribution of random vari...
Since histograms give little quantitative information about distribution, more detail descriptions a...
Characteristic Functions (cf) have been used to establish the convergence of several independent and...
Includes bibliographical references.This paper is concerned with a particularly useful function of p...
In this note, we show that if a sequence of moment generating functions Mn(t) converges pointwise to...
The probability mass function of a pair of discrete random variables (X,Y) is the function f(x,y)=P(...
The theory of Fourier transforms of generalized functions is used to extract general formulas for th...
45 pages, 1 article*Computing Tail Probabilities by Numerical Fourier Inversion: the Absolutely Con...
This thesis will focus on generating the probability mass function using Fibonacci sequenceas the co...
This book contains a systematic treatment of probability from the ground up, starting with intuitive...
These Lecture Slide Notes have been used for a two-quarter graduate level sequence in probability co...
Two deficiencies in using moment-generating functions are given and illustrated with examples. Many ...
The canonical way to establish the central limit theorem for i.i.d. random variables is to use chara...
summary:Part II of the paper aims at providing conditions which may serve as a bridge between existi...