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Monday, July 20, 2020 | History

2 edition of **Generating gamma distributed variates for computer simulation models** found in the catalog.

Generating gamma distributed variates for computer simulation models

Morton B Berman

- 246 Want to read
- 29 Currently reading

Published
**1971**
by Rand Corporation in Santa Monica, Calif
.

Written in English

- Digital computer simulation

**Edition Notes**

Series | Rand Corporation. Rand report -- R-641-PR, R (Rand Corporation) -- R-641-PR |

The Physical Object | |
---|---|

Pagination | 43 p. |

Number of Pages | 43 |

ID Numbers | |

Open Library | OL15267574M |

Abstract. Generating pseudo random object is one of the key issues in computer simulation of complex systems. Most earlier systems employ independent and identically distributed random variables, while those of real processes often show nontrivial autocorrelation. Basic simulation modeling. The nature of simulation. Systems, models, and simulation. Discrete-event simulation. Simulation of a single-server queueing system. Simulation of an inventory system. Distributed simulation. Steps in a simulation study. Other types of simulation. Advantages, disadvantages, and pitfalls of simulation. Modeling complex systems.

Department of Mathematics and Computer Science Acceptance-Rejection method If X is N.0;1/, then the density of jXjis given by f.x/D 2 p 2ˇ ex2=2; x >0: Now the function g.x/D p 2e=ˇex majorizes f. This leads to the following algorithm: te an exponential Y with mean 1; te U from U.0;1/, independent of Y;. This paper describes a numerical technique for the generation of beta random variates where the beta parameters are not limited to integer values. By not limiting parameters to integer values, one must evaluate the beta normalizing constant as a gamma function rather than as a .

2 Generating Random Variates Here, we present a quick overview of the methods used to generate random variates with a given probability distribution function (PDF). We assume that the computer used can generate random variates that are uniformly distributed over the interval [0;1]. There are two techniques for generating nonuniform random. 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 .

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