Binomial random variate generation

  title={Binomial random variate generation},
  author={Voratas Kachitvichyanukul and Bruce W. Schmeiser},
  journal={Commun. ACM},
Existing binomial random-variate generators are surveyed, and a new generator designed for moderate and large means is developed. The new algorithm, BTPE, has fixed memory requirements and is faster than other such algorithms, both when single, or when many variates are needed. 

Tables and Topics from this paper

History of random variate generation
  • M. Kuhl
  • Computer Science
  • 2017 Winter Simulation Conference (WSC)
  • 2017
A history of random variate generation including distribution sampling methods used prior to the introduction of digital computers, as well as the evolution of random Variate generators for continuous and discrete distributions and stochastic point processes are presented. Expand
Chapter 4 Nonuniform Random Variate Generation
This chapter provides a survey of the main methods in nonuniform random variate generation, and highlights recent research on the subject, before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods. Expand
Fast Generation of Discrete Random Variables
We describe two methods and provide C programs for generating discrete random variables with functions that are simple and fast, averaging ten times as fast as published methods and more than fiveExpand
Sampling from Discrete and Continuous Distributions with C-Rand
C-RAND is a system of Turbo-C routines and functions intended for use on microcomputers. It contains up-to-date random number generators for more than thirty univariate distributions. For someExpand
The ratio of uniforms approach for generating discrete random variates
Abstract The most efficient algorithms for sampling from classical discrete distributions are based upon the acceptance/rejection principle. They are complicated and not easy to understand. ByExpand
The generation of binomial random variates
The transformed rejection method, a combination of inversion and rejection, which can be applied to various continuous distributions, is well suited to generate binomial random variates as well. TheExpand
Simulating Size-constrained Galton-Watson Trees
  • L. Devroye
  • Mathematics, Computer Science
  • SIAM J. Comput.
  • 2012
We discuss various methods for generating random Galton-Watson trees conditional on their sizes being equal to $n$. A linear expected time algorithm is proposed.
Generation of random objects
  • L. Devroye
  • Computer Science, Mathematics
  • WSC '92
  • 1992
We illustrate the paradigm that various random objects defined in terms of random processes can be generated quite efficiently without actually ‘running” or ‘simulating” the defining random process.Expand
Algorithm 678: BTPEC: sampling from the binomial distribution
The FORTRAN implementation of an exact, uniformly fast algorithm for generating the binomial, random variables is presented. The algorithm is numerically stable and is faster than other publishedExpand
Non-Uniform Random Variate Generation
This is a survey of the main methods in non-uniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, andExpand


Binomial random variate generation
A new binomial random-variate generator designed for moderate and large means is developed, which has fixed memory requirements and is fast and scalable. Expand
On Generating Random Variates from an Empirical Distribution
Abstract This note presents a method for generating a sequence of random variates from an empirical distribution. Computational results show that the proposed method requires less computation timeExpand
An Efficient Method for Generating Discrete Random Variables with General Distributions
The fast generation of discrete random variables with arbitrary frequency distributions is discussed. The proposed method is related to rejection techniques but differs from them in that all samplesExpand
Random Variate Generation: A Survey.
Abstract : The state of the art of generating random variates on a digital computer is surveyed. General concepts are presented, followed by criteria for comparing algorithms. The literature isExpand
Beta Variate Generation via Exponential Majorizing Functions
Two acceptance/rejection algorithms for generating random variates from the beta distribution are developed and are relatively insensitive to parameter values and are faster than any previously published algorithms. Expand
Recent Developments in the Computer Generation of Poisson Random Variables
Two recent methods of generating samples on a computer from the Poisson distribution are compared with those in an earlier survey. Recommendations are made for algorithms which are either compact orExpand
Poisson Random Variate Generation.
Abstract : Approximate algorithms have long been the only available methods for generating Poisson random variates when the mean is large. A new algorithm is developed that is exact, has executionExpand
A Simple Algorithm for Generating Binomial Random Variables When N is Large
Abstract This article proposes a simple algorithm for generating binomial (N, p) random variables when N is large. The method involves looking mainly at medians in uniform (0, 1) samples of sizeExpand
Random variate generation
This paper updates the more than 300 references cited in last year's paper and concludes with a state of the art survey of methods for generating random variates on a digital computer. Expand
Computer Generation of Poisson Deviates from Modified Normal Distributions
Using efficient subprograms for generating uniform, exponential, alid normal deviates, the new algorithm is much faster than all competing methods. Expand