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Probability, Random Processes, and Ergodic Properties
Probability, Random Processes, and Ergodic Properties

Intransitive Dice
Intransitive Dice

An Introduction to Statistical Signal Processing
An Introduction to Statistical Signal Processing

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Measure Theoretic Probability P.J.C. Spreij (minor revisions by S.G.

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Introduction to Queueing Theory and Stochastic

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Introduction to Queueing Theory and Stochastic Teletraffic Models

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Measure Theoretic Probability P.J.C. Spreij

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Probability and Forensic Science

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here for U12 text. - Iowa State University

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Probability

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4 Combinatorics and Probability

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ON THE IMPLEMENTATION OF HUGE RANDOM OBJECTS 1

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The Curious Case of Noninteractive Commitments

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Probabilistic reasoning with answer sets

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StochModels Lecture 3: Probability Metrics

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Probability Models

Probability - OnlineStatBook
Probability - OnlineStatBook

Survival probabilities of weighted random walks
Survival probabilities of weighted random walks

Mansour`s Conjecture is True for Random DNF Formulas
Mansour`s Conjecture is True for Random DNF Formulas

Poisson Processes and Applications in Hockey
Poisson Processes and Applications in Hockey

Combinatorial Probability
Combinatorial Probability

Random Variables
Random Variables

A Parallel Repetition Theorem for Any Interactive Argument
A Parallel Repetition Theorem for Any Interactive Argument

Introductory lecture notes on Markov chains and random walks
Introductory lecture notes on Markov chains and random walks

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Random variable

In probability and statistics, a random variable, aleatory variable or stochastic variable is a variable whose value is subject to variations due to chance (i.e. randomness, in a mathematical sense). A random variable can take on a set of possible different values (similarly to other mathematical variables), each with an associated probability, in contrast to other mathematical variables.A random variable's possible values might represent the possible outcomes of a yet-to-be-performed experiment, or the possible outcomes of a past experiment whose already-existing value is uncertain (for example, due to imprecise measurements or quantum uncertainty). They may also conceptually represent either the results of an ""objectively"" random process (such as rolling a die) or the ""subjective"" randomness that results from incomplete knowledge of a quantity. The meaning of the probabilities assigned to the potential values of a random variable is not part of probability theory itself but is instead related to philosophical arguments over the interpretation of probability. The mathematics works the same regardless of the particular interpretation in use.The mathematical function describing the possible values of a random variable and their associated probabilities is known as a probability distribution. Random variables can be discrete, that is, taking any of a specified finite or countable list of values, endowed with a probability mass function, characteristic of a probability distribution; or continuous, taking any numerical value in an interval or collection of intervals, via a probability density function that is characteristic of a probability distribution; or a mixture of both types. The realizations of a random variable, that is, the results of randomly choosing values according to the variable's probability distribution function, are called random variates.The formal mathematical treatment of random variables is a topic in probability theory. In that context, a random variable is understood as a function defined on a sample space whose outputs are numerical values.
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