Designing a Random Number Generator
Introduction
Random Number Generator is a computational routine or a physical device that produces numbers which don’t have any pattern in them. Although using computational algorithms involves adding pattern to the resulting sequence of numbers.
We focussed on generating uniformly distributed random numbers between (0,1) since the same distribution can be used to get numbers from different ranges. We all know that LCG is well known method for giving a sequence of randomized numbers using linear equation but it still has some issues.
We have done 9 different tests on our RNG and have come up with quantitative results. Some of the tests performed are single level test and some are two level tests. we would also like to tell that some of these tests are Diehard tests [http://en.wikipedia.org/wiki/Diehard_tests].
Single level test- It’s a 1st level test observes the p-value, where p is a measure of uniformity. If p-value is extremely close to 0 to 1 then the generator fails since it produced value with very high uniformity (close to 1) or it is not uniform at all.
Two level test- Creating ‘N’ independent copies of base test having n sample size ‘n’ each. So, total sample size is ‘N*n’.
The Test gives us a ‘p’ value(Ref-http://shazam.econ.ubc.ca/intro/diehard.htm.), we would like present here a discussion on the p-value of the test.
Those p-values are obtained by p=F(X), where F is the assumed distribution of the sample random variable X---often normal. But that assumed F is just an asymptotic approximation, for which the fit will be worstin the tails. Thus you should not be surprised with occasional p-values near 0 or 1, such as .0012 or .9983. When a bit stream really FAILS BIG, you w...
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... of the Utility called TestU01 which has some predefined test suites for sequences of uniform random numbers over the interval (0,1).
Conclusion
We designed an RNG which is combination of 4 simple generators. we have tested and shown that the final RNG gives uniform distribution and passes 9 different complex tests which suggests that the designed RNG is enough random and can be used for simulation studies or other purposes.
Empirical studies also proves that by combining two or more simple generators, by means of a simple operations such as +, -, * or (exclusive-or), provides a composite with better randomness than either of the components[ G. Marsaglia. A current view of random number generators, 1984].
References
1. G. Marsaglia. A current view of random number generators, Computer Science and Statistics: 16th Symposium on the Interface, Atlanta, 1984.
New bitcoin are created by the intensive user task called “mining.” A miner solves mathematical algorithms w...
All over the world there are millions of people use credit card and on-line shopping. Every individual gets different numbers for credit card and for transcription of on-line-shopping. Where did all this number come from? Are the numbers in order? No, those numbers are made by RSA algorithm.
... that the encoding system by W. K. Wong, D. W. Cheung, E. Hung, B. Kao, and N. Mamoulis in [24] can be broken without using context-specific information. The success of the attacks in [25] mainly relies on the existence of unique, common, and fake items, defined by W. K. Wong, D. W. Cheung, E. Hung, B. Kao, and N. Mamoulis in [24]; our scheme does not create any such items, and the attacks by Y. Lindell and B. Pinkas in [5] are not applicable to our scheme. Tai et al. [9] assumed the attacker knows exact frequency of single items, similarly to us.
This is a series of test ran on a being S.A.M. aka Supersized Animalistic Monster. There are five test that were conducted label A-E.
Introduction to the basic concepts of probability and statistics with discussion of applications to computer science.
..., M., Oort, F., & Sprangers, M. (2013). Significance, truth and proof of p values:
To make sure it is a fair test; the procedure is repeated a couple of
Pierre Simon Laplace on Probability and Statistics . (n.d.). In cerebro.xu.edu. Retrieved March 24, 2014, from http://cerebro.xu.edu/math/Sources/Laplace/
n hypothesis of the experiment is that the group containing four members will perform better than the group containing two members. This is the foundation from which we have conducted our experiment.
... would need 2 × 34 = 162 observations to be able to fully explore the parameter space. Due to this “combinatorial explosion”, it is more common in scientific and industrial practice is to use a fractional-factorial experiment (FFE).
Let us see now how this algorithm works. The algorithms randomly creates solutions. Each one of these solutions has a fitness value based on some criteria. Those solutions of a specific problem are also called Phenotype, while the encoding of each solution is called Genotype. We refer on Representation as the procedure of establish the mapping between genotypes and phenotypes. Representation is used as in two different ways. As mentioned before, representation establish the mapping between the genotype and the phenotype. This means that representation could encode ore decode the candidate solutions.
Using three test tubes with samples in instead of two test tubes with samples in.
List of the tests to be conducted, material to be tested, the location of sampling, the organization’s name that will perform the test, and the frequency of testing.
I mean lets say we made a tree diagram based on how many heads and tales we got. We would do this test over and over again till we could determine the likelihood that we had a higher or lower prospect of spinning a head or a tail:
of the JUNG Typology test and the DISC Assessment actually proved to be fairly accurate to