June 20(Lecture 10)
... We say that the algorithm is in phase j when the size of the set under consideration is at most n(3/4)j but greater than n(3/4)j+1. So, to reach phase j, we kept running the randomized algorithm after the phase j – 1 until it is phase j. How much calls (or iterations) in each phases? Central: if at ...
... We say that the algorithm is in phase j when the size of the set under consideration is at most n(3/4)j but greater than n(3/4)j+1. So, to reach phase j, we kept running the randomized algorithm after the phase j – 1 until it is phase j. How much calls (or iterations) in each phases? Central: if at ...
Chernoff bounds, and some applications 1 Preliminaries
... As we are not able to improve Markov’s Inequality and Chebyshev’s Inequality in general, it is worth to consider whether we can say something stronger for a more restricted, yet interesting, class of random variables. This idea brings us to consider the case of a random variable that is the sum of a ...
... As we are not able to improve Markov’s Inequality and Chebyshev’s Inequality in general, it is worth to consider whether we can say something stronger for a more restricted, yet interesting, class of random variables. This idea brings us to consider the case of a random variable that is the sum of a ...
RANDOM VARIABLES AND RANDOM NUMBERS
... • Density function, distribution functions, expectation values and variances for many general probability distributions can be found in mathematical handbooks. The English version of Wikipedia also provides a lot of information. ...
... • Density function, distribution functions, expectation values and variances for many general probability distributions can be found in mathematical handbooks. The English version of Wikipedia also provides a lot of information. ...
the range of two dimensional simple random walk
... Since we have S0 = 0 by assumption, we use pn (x) for pn (x, 0). Note that pn describes the distribution of the location variable after n steps. Our goal is to give an estimation of the size of the two dimensional random walk range. Throughout the paper, the term step and time are interchangeable, a ...
... Since we have S0 = 0 by assumption, we use pn (x) for pn (x, 0). Note that pn describes the distribution of the location variable after n steps. Our goal is to give an estimation of the size of the two dimensional random walk range. Throughout the paper, the term step and time are interchangeable, a ...
Lecture 6
... statistics for summarizing our data • We want to make probability statements about the significance of our statistics • Eg. In Stat111, mean(height) = 66.7 inches • What is the chance that the true height of Penn students is between 60 and 70 inches? ...
... statistics for summarizing our data • We want to make probability statements about the significance of our statistics • Eg. In Stat111, mean(height) = 66.7 inches • What is the chance that the true height of Penn students is between 60 and 70 inches? ...
Randomness
Randomness is the lack of pattern or predictability in events. A random sequence of events, symbols or steps has no order and does not follow an intelligible pattern or combination. Individual random events are by definition unpredictable, but in many cases the frequency of different outcomes over a large number of events (or ""trials"") is predictable. For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will occur twice as often as 4. In this view, randomness is a measure of uncertainty of an outcome, rather than haphazardness, and applies to concepts of chance, probability, and information entropy.The fields of mathematics, probability, and statistics use formal definitions of randomness. In statistics, a random variable is an assignment of a numerical value to each possible outcome of an event space. This association facilitates the identification and the calculation of probabilities of the events. Random variables can appear in random sequences. A random process is a sequence of random variables whose outcomes do not follow a deterministic pattern, but follow an evolution described by probability distributions. These and other constructs are extremely useful in probability theory and the various applications of randomness.Randomness is most often used in statistics to signify well-defined statistical properties. Monte Carlo methods, which rely on random input (such as from random number generators or pseudorandom number generators), are important techniques in science, as, for instance, in computational science. By analogy, quasi-Monte Carlo methods use quasirandom number generators.Random selection is a method of selecting items (often called units) from a population where the probability of choosing a specific item is the proportion of those items in the population. For example, with a bowl containing just 10 red marbles and 90 blue marbles, a random selection mechanism would choose a red marble with probability 1/10. Note that a random selection mechanism that selected 10 marbles from this bowl would not necessarily result in 1 red and 9 blue. In situations where a population consists of items that are distinguishable, a random selection mechanism requires equal probabilities for any item to be chosen. That is, if the selection process is such that each member of a population, of say research subjects, has the same probability of being chosen then we can say the selection process is random.