Study Materials
... possible to predict which face shows up. Such experiments where the outcomes cannot be predicted with certainty are called Random experiments. Random experiment and phenomena are the heart of probability theory which owes its origin to the study of games of chance or gambling. B.PASCAL(1623-1662)and ...
... possible to predict which face shows up. Such experiments where the outcomes cannot be predicted with certainty are called Random experiments. Random experiment and phenomena are the heart of probability theory which owes its origin to the study of games of chance or gambling. B.PASCAL(1623-1662)and ...
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... This course introduces stochastic processes as models for a variety of phenomena in sciences. Following a brief review of basic concepts in probability the course will introduce stochastic processes that are popular in scientific applications, such as discrete time Markov chains, the Poisson process ...
... This course introduces stochastic processes as models for a variety of phenomena in sciences. Following a brief review of basic concepts in probability the course will introduce stochastic processes that are popular in scientific applications, such as discrete time Markov chains, the Poisson process ...
ppt
... If we choose a permutation at random, the probability that it will produce the same minhash values for two sets is the same as the Jaccard similarity of those sets. • Thus, if we have the signatures of two sets S and T, we can estimate the Jaccard similarity of S and T by the fraction of correspondi ...
... If we choose a permutation at random, the probability that it will produce the same minhash values for two sets is the same as the Jaccard similarity of those sets. • Thus, if we have the signatures of two sets S and T, we can estimate the Jaccard similarity of S and T by the fraction of correspondi ...
A "No Panacea Theorem" for Multiple Classifier Combination
... greater than 1 − . For this two-classifier, two-class problem, every combination algorithm needs to generate a combination function F (x1 , x2 ) based on the training data X1 , X2 , . . . , XM +N . But, as can be seen from equation (2), the performance of the combination algorithm is not only assoc ...
... greater than 1 − . For this two-classifier, two-class problem, every combination algorithm needs to generate a combination function F (x1 , x2 ) based on the training data X1 , X2 , . . . , XM +N . But, as can be seen from equation (2), the performance of the combination algorithm is not only assoc ...
Estimating sigma in a normal distribution - Ing-Stat
... column for each sample size, remembering that 2 =1). This is as expected as the estimator is unbiased. We also see that the m.s.e., estimated from the data, corresponds good to the theoretical value. The remarks are also valid for the second estimator, but as it is biased it misses the true value. ...
... column for each sample size, remembering that 2 =1). This is as expected as the estimator is unbiased. We also see that the m.s.e., estimated from the data, corresponds good to the theoretical value. The remarks are also valid for the second estimator, but as it is biased it misses the true value. ...
Probability 1 (F)
... Please also note that the layout in terms of fonts, answer lines and space given to each question does not reflect the actual papers to save space. These questions have been collated by me as the basis for a GCSE working party set up by the GLOW maths hub - if you want to get involved please get in ...
... Please also note that the layout in terms of fonts, answer lines and space given to each question does not reflect the actual papers to save space. These questions have been collated by me as the basis for a GCSE working party set up by the GLOW maths hub - if you want to get involved please get in ...
A Note on Coloring Random k-Sets
... then the algorithm colors x the “other” color, Blue. If there are several such A the algorithm selects one at random and colors its x accordingly. Note: if the x so colored lies in one A that is otherwise Red and one that is otherwise Blue then this will lead to (0) in the next round. (2) If not (0) ...
... then the algorithm colors x the “other” color, Blue. If there are several such A the algorithm selects one at random and colors its x accordingly. Note: if the x so colored lies in one A that is otherwise Red and one that is otherwise Blue then this will lead to (0) in the next round. (2) If not (0) ...
Factorising numbers with a Bose
... for given (asymptotically) large integers N , i.e., on the probability of finding k factors in a randomly selected factorisation of a large N . We will proceed as follows: In section II we state recursion relations required for the numerical evaluation of the exact quantities Φ(N, k), deferring the ...
... for given (asymptotically) large integers N , i.e., on the probability of finding k factors in a randomly selected factorisation of a large N . We will proceed as follows: In section II we state recursion relations required for the numerical evaluation of the exact quantities Φ(N, k), deferring the ...
Review Lecture 3
... billion dollars of assets. The latter banks are called “large institutions.” The community bankers Council of the American bankers Association (ABA) conducts an annual survey of community banks. For the 110 banks that make up the sample in a recent survey, the mean assets are X = 220 (in millions of ...
... billion dollars of assets. The latter banks are called “large institutions.” The community bankers Council of the American bankers Association (ABA) conducts an annual survey of community banks. For the 110 banks that make up the sample in a recent survey, the mean assets are X = 220 (in millions of ...
"Lecture 1: Introduction to Random Walks and Diffusion."
... steps. It is impressive how the complicated collection of random walkers tends toward a simple, smooth distribution, at least in the central region. We now present a simple derivation of a generalization of Lord Rayleigh’s result, which will be covered again in more detail in subsequent lectures. Co ...
... steps. It is impressive how the complicated collection of random walkers tends toward a simple, smooth distribution, at least in the central region. We now present a simple derivation of a generalization of Lord Rayleigh’s result, which will be covered again in more detail in subsequent lectures. Co ...
4. Random Variables, Bernoulli, Binomial, Hypergeometric
... probability that the 2 of them are red? Now suppose that you draw 5 jelly beans out of the bag. What is the probability that 3 are red and 2 are green? This is an example of a Hypergeometric random variable. The characteristic is “being red”. The population is the jelly beans in the bag, so N = 10. ...
... probability that the 2 of them are red? Now suppose that you draw 5 jelly beans out of the bag. What is the probability that 3 are red and 2 are green? This is an example of a Hypergeometric random variable. The characteristic is “being red”. The population is the jelly beans in the bag, so N = 10. ...
Notes on hypothesis testing
... Hypothesis testing Sometimes what we care about isn’t necessarily the precise value of an estimator, but rather whether it is significant: in other words, does a sample mean support or contradict some pre-existing idea about the mean. For example, suppose I want a mean grade on the first assignment ...
... Hypothesis testing Sometimes what we care about isn’t necessarily the precise value of an estimator, but rather whether it is significant: in other words, does a sample mean support or contradict some pre-existing idea about the mean. For example, suppose I want a mean grade on the first assignment ...
Institute of Actuaries of India May 2012 Examinations Indicative Solutions
... Institute of Actuaries of India Soln.11. (a) Under Design 1, the weights relate to different sets of individuals. As no two individuals were the same, we can consider the two samples to be independent. Under Design 2, the weights relate to same sets of individuals measured once before the campaign ...
... Institute of Actuaries of India Soln.11. (a) Under Design 1, the weights relate to different sets of individuals. As no two individuals were the same, we can consider the two samples to be independent. Under Design 2, the weights relate to same sets of individuals measured once before the campaign ...
Problem of the Day 1. You have 8 nice shirts, 5 pairs of nice pants
... How many different license plates with 5 numbers and 3 letters can be made? (How many if no number or letter can be repeated) ...
... How many different license plates with 5 numbers and 3 letters can be made? (How many if no number or letter can be repeated) ...
Definition of the Domain for Summative Evaluation
... – probability of events in a geometric context or another type of context – odds of events occurring in a geometric context or another type of context – determining the event with the highest probability of occurence – problem that involves calculating the probability or the odds of an event occurri ...
... – probability of events in a geometric context or another type of context – odds of events occurring in a geometric context or another type of context – determining the event with the highest probability of occurence – problem that involves calculating the probability or the odds of an event occurri ...
Probability box
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A probability box (or p-box) is a characterization of an uncertain number consisting of both aleatoric and epistemic uncertainties that is often used in risk analysis or quantitative uncertainty modeling where numerical calculations must be performed. Probability bounds analysis is used to make arithmetic and logical calculations with p-boxes.An example p-box is shown in the figure at right for an uncertain number x consisting of a left (upper) bound and a right (lower) bound on the probability distribution for x. The bounds are coincident for values of x below 0 and above 24. The bounds may have almost any shapes, including step functions, so long as they are monotonically increasing and do not cross each other. A p-box is used to express simultaneously incertitude (epistemic uncertainty), which is represented by the breadth between the left and right edges of the p-box, and variability (aleatory uncertainty), which is represented by the overall slant of the p-box.