Photos and text by Rob Bicevskis Various books and other documents often refer to something called "Char. Why do we want char?

Universal hash families Randomization is a fundamental technique in algorithm design, that allows programs to run quickly when the average-case behavior of an algorithm is better than the worst-case behavior. It is also heavily used in games, both in entertainment and gambling.

The latter application gives the only known example of a programmer being murdered for writing bad code http: Generating random values in C If you want random values in a C program, there are three typical ways of getting them, depending on how good i.

The rand function from the standard library E. There are no particularly strong guarantees about the quality of random numbers that rand returns, but it should be good enough for casual use, and has the advantage that as part of the C standard you can assume it is present almost everywhere.

Note that rand is a pseudorandom number generator: It is also the case that the initial seed is fixed, so that the program above will print the same value every time you run it this is a feature: If you want to get different sequences, you need to seed the random number generator using srand.

A typical use might be: Note that this still might give repeated values if you run it twice in the same second, and it's extremely dangerous if you expect to distribute your code to a lot of people who want different results, since two of your users are likely to run it twice in the same second.

Better pseudorandom number generators There has been quite a bit of research on pseudorandom number generators over the years, and much better pseudorandom number generators than rand are available. The current champion for simulation work is the Mersenne Twister, which runs about 4 times faster than rand in its standard C implementation and passes a much wider battery of statistical tests.

Its English-language home page is at http: As with rand, you still need to provide an initial seed value. There are also cryptographically secure pseudorandom number generators, of which the most famous is Blum Blum Shub.

These cannot be predicted based on their output if seeded with a true random value under certain cryptographic assumptions: Unfortunately, cryptographic PRNGs are usually too slow for day-to-day use. These can be opened for reading like ordinary files, but the values read from them are a random sequence of bytes including null characters.

For example, suppose you want to simulate a die roll for your video craps machine, but you don't want to get whacked by Johnny "The Debugger" when the Nevada State Gaming Commission notices that is coming up slightly less often than it's supposed to.

A natural thing to try would be to take the output of rand mod 6: So 1 and 2 are slightly more likely to come up than 3, 4, 5, or 6.

This can be particularly noticeable if we want a uniform variable from a larger range, e. We can avoid this with a technique called rejection sampling, where we reject excess parts of the output range of rand. For rolling a die, the trick is to reject anything in the last extra bit of the range that is left over after the largest multiple of the die size.

Here's a routine that does this, returning a uniform value in the range 0 to n-1 for any positive n, together with a program that demonstrates its use for rolling dice: Here's a program that generates random primes: Randomized search This is essentially rejection sampling in disguise.

Suppose that you want to find one of many needles in a large haystack. But you may find that your good friend the adversary has put all the needles at the end of your list.

Picking candidate at random is likely to hit a needle faster if there are many of them. Here is a silly routine that quickly finds a number whose high-order bits match a particular pattern: In contrast, this deterministic version will take a lot longer for nonzero patterns: The usual solution is to either accept a small probability of failure, or interleave a deterministic backup algorithm that always works.Welcome to the Free E-mail Database.

This page is a public service to provide E-mail addresses for any purpose you may need. Drawing from a constantly-updated database, we offer up free lists of E-mail address to hundreds of users per day! Probabilistic design is a discipline within engineering design.

It deals primarily with the consideration of the effects of random variability upon the performance of an engineering system during the design phase.

Student Sports Projects in a Statistics Course Robert L. Wardrop Department of Statistics this approach, see Rossman [1], and Wardrop [2], [3], and [4].

at hitting a golf ball from a fairway lie with a 3 wood or with a 3 iron. Finally, Brian was a student in the ROTC program. As part of his training, Brian was required to run in combat. Figure Sample Launch Failure Probability Tree ..

50 Figure Probability Tree Demonstrating the Computation of Total Vehicle Failure Probability Based on “Prior” Failure Probability Estimates for Each Stage .. 52 Figure Char: Char, (Salvelinus), any of several freshwater food and game fishes distinguished from the similar trout by light, rather than black, spots and by a boat-shaped bone (vomer) that is toothed only in front, on the roof of the mouth.

Chars are of the trout and salmon family, Salmonidae, and often have. Create beautiful infographics, flyers, posters, presentations and reports easily with absolutely no design experience. Try Piktochart, it's free! We've just launched Piktochart for Teams.

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Normal Approximation to Binomial | STAT /