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### Linear Congruential Generators

• Most simulation algorithms are based on standard random number
generators,

• whose goal is to generate sequences of numbers in
the unit interval . These are the so-called standard
pseudo-random numbers,
• which can be regarded as realizations of independent and on uniformly distributed random variables .
• A commonly established procedure to generate standard pseudo-random
numbers is the following linear congruential method,

As a next step we will solve the recursion equation
(1), i.e., we will show how the number that
has been recursively defined in (1) can be
expressed directly by the initial value and the
parameters , and .

Proof
Remarks

We will now mention some (sufficient and necessary) conditions for
the parameters , , and , respectively, ensuring
that the maximal possible period is obtained.

A proof of Theorem 3.2 using results from
number theory (one of them being Fermat’s little theorem) can be
found e.g.

• in Section 2.7 of B.D. Ripley Stochastic Simulation, J.
Wiley & Sons, New York (1987) or
• in Section 3.2 of D.E. Knuth (1997) The Art of Computer
• We also refer to these two texts for the discussion
• of other generators for standard pseudo-random numbers like
nonlinear congruential generators, shift-register generators and
lagged Fibonacci generators as well as their combinations,
• alternative conditions for the parameters , , and of
the linear congruential generator defined in (1),
• ensuring the generation of sequences whose period is as large as possible and also exhibiting other desirable
properties.
• One of those properties is
• that the points formed by pairs
of consecutive pseudo-random numbers , are
uniformly spread over the unit square .
• The following numerical examples illustrate that relatively small
changes of the parameters and can result in completely
different point patterns .
• Further details can be found in the text by Ripley (1987) that has
been already mentioned and in the lecture notes by H. Künsch
(ftp://stat.ethz.ch/U/Kuensch/skript-sim.ps) that also contains the
following figures.
Figure 3:
Point patterns for pairs of consecutive
pseudo-random numbers for Figure 4:
Point patterns for pairs of consecutive
pseudo-random numbers for Figure 5:
Point patterns for pairs of consecutive
pseudo-random numbers for     Next: Statistical Tests
Up: Generation of Pseudo-Random Numbers
Previous: Simple Applications; Monte-Carlo Estimators
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Ursa Pantle
2006-07-20

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