Random Number Generation

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Random numbers are used in many places. The
appearance of the sounds of nature, the bus run time from the same
stops, travel time between home and the office every day, the variable
load of each month, predicting the flip of a coin
when he threw up, predict the sum of two sides of a given when lifting. All these phenomena have a certain degree of randomness.
The random numbers are generated in the sequence numbers that can not be predicted. For
example, if the equation of a variable Y = X + 5 * 4 X can be
completely predicted from random numbers Y. occur between 0 and 1 and
are perfectly normal distribution.


For example, when produced in a large number of random numbers occur equally between 0 and 1 on the Gaussian curve. That is to say between 0 and 0.1 would have the same amount of numbers between 0.8 and 0.9. Random
numbers are required in the simulation studies such as the simulation
of driving time between two locations, simulating a pair of dice,
simulating.
There are many different types of random number generators the most important is the linear congruence generator.
These numbers are generated when they have to respond to numerous tests defined for the frequency statistics, etc. normally, but the numbers generated by many generators are pseudo-random. It
is very important to use random numbers perfect to analyze systems
using discrete event simulation models so we can get replicas of the
simulation models are statistically independent.
These are also generated using the sounds of nature and other natural phenomena.
random random variables can also be generated using these numbers. These random value may be uniform, Fish, etc. normal,
in this case, would be required to generate the distribution function,
for example, a number such as “time to wake up in the morning” may vary
from 5:30 to 9: am 12. In the case where there is a likelihood
equal to wake up at any time 05: 30-09h00, then wake up time can be
given that the wake-up time 5:30 = 210 + r * r is a number between 0 and
1.
Now
if you realize in the above equation if r is generated uniformly
between 0 and 1, the time of awakening vary uniformly 5: 30-9 and
thirties.
But
the r distribution is not normal, then you might find several r values
​​generated between 0.4 and 0.5, for example, 0.9 to 1. Thus, when the
act of awakening is a simulation find
several data values ​​between 6:54 ET 7:15 produce a false inference or false conclusion that the simulation model. It is therefore very important to use a generator that produces normally distributed data.
The author is a master double scientific research in the ISE and IT. He worked for many years in leading companies of IT services.


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Random Number Generation

Generation, Number, Random, Random Number Generation

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