Publication

Stochasticity of probabilistic systems: Analysis methodologies case-study

Martin Hasler, Karl Aberer, Abhisek Datta
2005
Conference Papers

Abstract

We do a case study of two different analysis techniques for studying the stochastic behavior of a randomized system/algorithms: (i) The first approach can be broadly termed as a mean value analysis (MVA), where the evolution of the mean state is studied assuming that the system always actually resides in the mean state. (ii) The second approach looks at the probability distribution function of the system states at any time instance, thus studying the evolution of the (probability mass) distribution function (EoDF).

Official source

https://infoscience.epfl.ch/entities/publication/a3564840-234b-434d-83c6-a3e8af77d3e8

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Martin Hasler, Karl Aberer, Abhisek Datta
2005
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a3564840-234b-434d-83c6-a3e8af77d3e8

About this result

Related concepts (27)

Cumulative distribution function

In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable , or just distribution function of , evaluated at , is the probability that will take a value less than or equal to . Every probability distribution supported on the real numbers, discrete or "mixed" as well as continuous, is uniquely identified by a right-continuous monotone increasing function (a càdlàg function) satisfying and .

Joint probability distribution

Given two random variables that are defined on the same probability space, the joint probability distribution is the corresponding probability distribution on all possible pairs of outputs. The joint distribution can just as well be considered for any given number of random variables. The joint distribution encodes the marginal distributions, i.e. the distributions of each of the individual random variables. It also encodes the conditional probability distributions, which deal with how the outputs of one random variable are distributed when given information on the outputs of the other random variable(s).

Probability mass function

In probability and statistics, a probability mass function is a function that gives the probability that a discrete random variable is exactly equal to some value. Sometimes it is also known as the discrete probability density function. The probability mass function is often the primary means of defining a discrete probability distribution, and such functions exist for either scalar or multivariate random variables whose domain is discrete.

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