TAILIEUCHUNG - Random Numbers part 2

As for references on this subject, the one to turn to first is Knuth [1]. Then try [2]. Only a few of the standard books on numerical methods [3-4] treat topics relating to random numbers. | Uniform Deviates 275 As for references on this subject the one to turn to first is Knuth 1 . Then try 2 . Only a few of the standard books on numerical methods 3-4 treat topics relating to random numbers. CITED REFERENCES AND FURTHER READING Knuth . 1981 SeminumericalAlgorithms 2nd ed. vol. 2 of The Art of ComputerProgramming Reading MA Addison-Wesley Chapter 3 especially . 1 Bratley P. Fox . and Schrage . 1983 A Guide to Simulation New York SpringerVerlag . 2 Dahlquist G. and Bjorck A. 1974 Numerical Methods Englewood Cliffs NJ Prentice-Hall Chapter 11. 3 Forsythe . Malcolm . and Moler . 1977 Computer Methods for Mathematical Computations Englewood Cliffs NJ Prentice-Hall Chapter 10. 4 Uniform Deviates Uniform deviates are just random numbers that lie within a specified range typically 0 to 1 with any one number in the range just as likely as any other. They are in other words what you probably think random numbers are. However we want to distinguish uniform deviates from other sorts of random numbers for example numbers drawn from a normal Gaussian distribution of specified mean and standard deviation. These other sorts of deviates are almost always generated by performing appropriate operations on one or more uniform deviates as we will see in subsequent sections. So a reliable source of random uniform deviates the subject of this section is an essential building block for any sort of stochastic modeling or Monte Carlo computer work. System-Supplied Random Number Generators Most C implementations have lurking within a pair of library routines for initializing and then generating random numbers. In ANSI C the synopsis is include define RAND_MAX . void srand unsigned seed int rand void You initialize the random number generator by invoking srand seed with some arbitrary seed. Each initializing value will typically result in a different random sequence or a least a different starting point in some one enormously long sequence.

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