12.0 Statistical Graphics and RNG

Transcription

1 12.0 Statistical Graphics and RNG 1 Answer Questions Statistical Graphics Random Number Generators

2 12.1 Statistical Graphics 2 John Snow helped to end the 1854 cholera outbreak through use of a statistical graphic based on a city map of London. The map shows the pattern of the disease outbreak, and illustrates the importance of exception analysis. Snow was Queen Victoria s physician and a protege of Florence Nightingale. He also found a smart way to estimate the literacy rate. Guess how he did it?

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4 The second graphic shows the age-adjusted incidence of stomach cancer for white males, for cases between We can compare that with a similar map for Is there a gender difference? What is going on in Nevada? 4 What is going on in New Mexico? What is going on in Wisconsin, Minnesota, and North Dakota? What about Pittsburgh? What about Maine? How do we interpret single-county hotspots?

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6 The third graphic shows the pedestrian fatality rates by state. Florida is the worst, and has the top five cities in the country. What might explain this (consider also New Mexico and Arizona). 6 The fourth graphic is by Charles-Joseph Minard; Richard Tufte hails it as the best statistical graphic ever. It shows the size of Napoleon s army in , as he attacks Czar Alexander III in Moscow and then retreats. The graphic includes information on: location (two dimensions) time temperature size of the army

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11 12.2 Random Numbers In order to generate random numbers, it is sufficient to generate random binary strings. 11 Toss a fair coin an infinite number of times, with heads being 0 and tails being 1, to get a sequence X 1, X 2,.... This can be converted into a random number U that is uniformly distributed on [0, 1] by U = i=1 X i 2 i. If you have a random number that is uniform on [0, 1], then the random number X = F 1 (U) is a random draw from the distribution F(x). So all you need for any kind of random number is a set of random coin tosses.

12 Real coins aren t random enough, or practical for the two main applications: computer simulations cryptosecurity. 12 Good Random Number Generators (RNGs) are fast, repeatable (i.e., have a seed), do not cycle, have sensitive dependence on the seed, and pass statistical tests for randomness. In practice, there are three strategies for building random number generators (RNGs): Amplify physical (quantum) noise. Use provably hard algorithms (trapdoor codes), such as fractoring large numbers that are products of two primes. Use linear congruential generators.

13 The first method has never been able to pass statistical tests for randomness. The sequences always show patterns introduced by the amplification mechanism. 13 The second method is widely used in cryptography, but there are issues. It is not repeatable, in the sense needed for replicating a computer experiment. It cannot produce an infinite string of binary digits: eventually, you factor the number. And the big fear is that some clever mathematician will discover a new way for factoring large numbers. Nonetheless, trapdoor codes are wildly popular in cryptography, and quite reliable. RSA encryption is one famous example it is the basis for most on-line credit card transactions.

14 For simulation, computer games, and other applications, linear congruential generators are used. X n+1 (ax n + c) (mod m) where v w (mod m) means that v is the remainder when w is divided by m, and 14 X n is current random integer, X n+1 is the next random integer in the sequence m is the modulus (a very large integer) a and c are carefully chosen constants. The initial value, X 0, is called the seed of the linear congruential generator. The X i are written in binary.

15 Linear congruential generators are not perfect. There is some correlation in the sequence: if one uses them to plot points in an k-dimensional space, the points will lie upon up to m 1/k hyperplanes. 15 On the other hand, these are fast, use little memory, can have cycle time m, and are replicable if one archives the seed.

16 When one has a long sequence of binary random digits, One can try to test whether the sequence is random. One strategy is to do a series of hypothesis tests: 1. The null is that the proportions of 1s is 1/2; the alternative is that it is not The null is that the proportion of sequential pairs (0, 0) [and (0, 1), (1, 0), (1, 1)] is 1/4; the alternative is that it is not. 5. The null is that the proportion of sequential pairs (0, 0, 0) is 1/8; the null is that it is not; etc. You know how to make all of these tests. You could even adjust for multiple testing. But letting X i be 0 or 1 according to the oddness or eveness of the ith digit of π would pass all these tests.

17 It is provable that one cannot design a test that will eventually detect all possible patterned sequences. But one can design a sequence of tests that will discover many different kinds of patterns. Information theory has shown that a truly random sequence cannot be compressed. A string is comressible if it can be encoded in such a way that the coded version requires fewer bits than the original string. 17 So one way to test a random number generator is to feed its output into gzip, JPEG2000, and the Lempel-Ziv compression algorithms, and see if the result is substantially shorter. Another theorem: If sequence X 1, X 2,... is added to sequence Y 1, Y 2,... to produce Z 1, Z 2,... where Z i = X i + Y i (mod 2), then the Z sequence is at least as random as the most random of the X and Y sequences.