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The relations among inhibition and interference control functions: a latent-variable analysis.
Journal of experimental psychology. General, 2004This study used data from 220 adults to examine the relations among 3 inhibition-related functions. Confirmatory factor analysis suggested that Prepotent Response Inhibition and Resistance to Distractor Interference were closely related, but both were ...
N. Friedman, A. Miyake
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1965
Publisher Summary This chapter describes random variables. A random variable is a variable that assumes, as a result of a trial, only one of the set of possible values and with which is connected some field of events representing its occurrences in given sets, contained in the main field of events δ. Random variables may be both scalar and vector.
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Publisher Summary This chapter describes random variables. A random variable is a variable that assumes, as a result of a trial, only one of the set of possible values and with which is connected some field of events representing its occurrences in given sets, contained in the main field of events δ. Random variables may be both scalar and vector.
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1978
The major theorems of probability theory fall into a natural dichotomy2014;those which are analytic in character and those which are measure-theoretic. In the latter category are zero-one laws, the Borel-Cantelli lemma, strong laws of large numbers, and indeed any result which requires the apparatus of a probability space.
Henry Teicher, Yuan Shih Chow
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The major theorems of probability theory fall into a natural dichotomy2014;those which are analytic in character and those which are measure-theoretic. In the latter category are zero-one laws, the Borel-Cantelli lemma, strong laws of large numbers, and indeed any result which requires the apparatus of a probability space.
Henry Teicher, Yuan Shih Chow
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1994
Abstract Specializing the concepts of Chapter 7 to the case of real variables, this chapter introduces distribution functions, discrete and continuous distributions, and describes examples such as the binomial, uniform, Gaussian, Cauchy, and gamma distributions. It then treats multivariate distributions and the concept of independence.
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Abstract Specializing the concepts of Chapter 7 to the case of real variables, this chapter introduces distribution functions, discrete and continuous distributions, and describes examples such as the binomial, uniform, Gaussian, Cauchy, and gamma distributions. It then treats multivariate distributions and the concept of independence.
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Functions of Random Variables [PDF]
, 1983 Consider the notion of a “square-law” detector: If x is an input to the detector, then y = x 2 is its output or detected value. Consider next the case where x is a random variable with probability law p X (x) Then output y is also random. If so, what is its probability law PY(y)?
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ACM SIGSIM Simulation Digest, 1973
This note gives APL functions for generating random variables from 21 common statistical distributions:
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This note gives APL functions for generating random variables from 21 common statistical distributions:
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1976
In some parts of statistical inference it is customary to speak entirely in random variable terms, as in the analysis of variance. One considers his job finished when he writes the ratio of two independently distributed chi-square random variables, the denominator a central chi-square.
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In some parts of statistical inference it is customary to speak entirely in random variable terms, as in the analysis of variance. One considers his job finished when he writes the ratio of two independently distributed chi-square random variables, the denominator a central chi-square.
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Random Variables: Distributions
1999We will now consider not the probability of observing particular events but rather the events themselves and try to find a particularly simple way of classifying them. We can, for instance, associate the event “heads” with the number 0 and the event “tails” with the number 1.
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1983
Until now, all experiments E have had discrete events {A n} as their outputs, as in rolls of a die. On the other hand, everyday experience tells us that continuously random events often occur, as in the waiting time t for a train, or the position x at which a photon strikes the image plane.
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Until now, all experiments E have had discrete events {A n} as their outputs, as in rolls of a die. On the other hand, everyday experience tells us that continuously random events often occur, as in the waiting time t for a train, or the position x at which a photon strikes the image plane.
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