Stat 5101 Notes: Expectation
... A student asked why the theorem only applies to nonnegative functions. R That is because our theory is about absolute integrability. We say g(x) dx R exists if and only if |g(x)| dx exists. So all questions about existence are about nonnegative integrands |g(x)|. The theorem is just written without ...
... A student asked why the theorem only applies to nonnegative functions. R That is because our theory is about absolute integrability. We say g(x) dx R exists if and only if |g(x)| dx exists. So all questions about existence are about nonnegative integrands |g(x)|. The theorem is just written without ...
Lesson Plan - Dominant Recessive Sampling Basic Model
... user. When two parents contribute dominant or recessive alleles to their offspring, the recessive trait will always be “hidden” by the dominant trait. The trait that is expressed is known as an organism’s “phenotype,” while the set of alleles is known as its “genotype.” Have students explore genotyp ...
... user. When two parents contribute dominant or recessive alleles to their offspring, the recessive trait will always be “hidden” by the dominant trait. The trait that is expressed is known as an organism’s “phenotype,” while the set of alleles is known as its “genotype.” Have students explore genotyp ...
Mini-Lecture 6.1
... 2. When playing the Powerball lottery, in addition to guessing the value of five white balls, players also guess the “Powerball.” At the time of the semi-weekly drawings, 42 red balls are placed in a bin. One of these is selected as the Powerball. (Source: www.Powerball.com) a. Let the random varia ...
... 2. When playing the Powerball lottery, in addition to guessing the value of five white balls, players also guess the “Powerball.” At the time of the semi-weekly drawings, 42 red balls are placed in a bin. One of these is selected as the Powerball. (Source: www.Powerball.com) a. Let the random varia ...
PK b
... compute e(g, g)abc G2. We say that BDH is intractable if all polynomial time algorithms have a negligible advantage in solving BDH. ...
... compute e(g, g)abc G2. We say that BDH is intractable if all polynomial time algorithms have a negligible advantage in solving BDH. ...
Probability - HKMU Student Portal
... • If Joshua and Hassan attend Biostatistics lectures only once in a month and there is one Biostatistics lecture per week; what is the probability of finding both of them in a given lecture • What is the probability of finding either of them in a given lecture ...
... • If Joshua and Hassan attend Biostatistics lectures only once in a month and there is one Biostatistics lecture per week; what is the probability of finding both of them in a given lecture • What is the probability of finding either of them in a given lecture ...
Final test Statistics 2
... We reconsider the so-called paradox of Galilei: “When three fair dice are thrown, then there are six outcomes that all have 10 eyes in total: 631, 622, 541, 532, 442 and 433. To obtain a total of 9 eyes, there also are six outcomes: 621, 522, 531, 441, 432 and 333. Still it turns out that the total ...
... We reconsider the so-called paradox of Galilei: “When three fair dice are thrown, then there are six outcomes that all have 10 eyes in total: 631, 622, 541, 532, 442 and 433. To obtain a total of 9 eyes, there also are six outcomes: 621, 522, 531, 441, 432 and 333. Still it turns out that the total ...
ppt
... One-way functions are essential to the two guards password problem • Are we done? Given a noninteracive identification protocol want to define a one-way function • Define function f(r) as the mapping that Alice does in the setup phase between her random bits r and the information y given to Bob and ...
... One-way functions are essential to the two guards password problem • Are we done? Given a noninteracive identification protocol want to define a one-way function • Define function f(r) as the mapping that Alice does in the setup phase between her random bits r and the information y given to Bob and ...
Hardware random number generator
In computing, a hardware random number generator (TRNG, True Random Number Generator) is an apparatus that generates random numbers from a physical process, rather than a computer program. Such devices are often based on microscopic phenomena that generate low-level, statistically random ""noise"" signals, such as thermal noise, the photoelectric effect, and other quantum phenomena. These processes are, in theory, completely unpredictable, and the theory's assertions of unpredictability are subject to experimental test. A hardware random number generator typically consists of a transducer to convert some aspect of the physical phenomena to an electrical signal, an amplifier and other electronic circuitry to increase the amplitude of the random fluctuations to a measurable level, and some type of analog to digital converter to convert the output into a digital number, often a simple binary digit 0 or 1. By repeatedly sampling the randomly varying signal, a series of random numbers is obtained. The main application for electronic hardware random number generators is in cryptography, where they are used to generate random cryptographic keys to transmit data securely. They are widely used in Internet encryption protocols such as Secure Sockets Layer (SSL).Random number generators can also be built from ""random"" macroscopic processes, using devices such as coin flipping, dice, roulette wheels and lottery machines. The presence of unpredictability in these phenomena can be justified by the theory of unstable dynamical systems and chaos theory. Even though macroscopic processes are deterministic under Newtonian mechanics, the output of a well-designed device like a roulette wheel cannot be predicted in practice, because it depends on the sensitive, micro-details of the initial conditions of each use. Although dice have been mostly used in gambling, and in more recent times as ""randomizing"" elements in games (e.g. role playing games), the Victorian scientist Francis Galton described a way to use dice to explicitly generate random numbers for scientific purposes in 1890.Hardware random number generators generally produce a limited number of random bits per second. In order to increase the data rate, they are often used to generate the ""seed"" for a faster Cryptographically secure pseudorandom number generator, which then generates the pseudorandom output sequence.