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Probability - TeacherWeb
Probability - TeacherWeb

The Modelling of Random Phenomena
The Modelling of Random Phenomena

Lecture notes
Lecture notes

ON BERNOULLI DECOMPOSITIONS FOR RANDOM VARIABLES
ON BERNOULLI DECOMPOSITIONS FOR RANDOM VARIABLES

Lecture 17
Lecture 17

Binomial Distribution
Binomial Distribution

BINOMIAL THEOREM
BINOMIAL THEOREM

Conditional Probability and Independent Events
Conditional Probability and Independent Events

... Example 3: When rolling a single die, what is the probability of rolling a prime given that the number rolled is even? ...
A Tail Bound for Read-k Families of Functions
A Tail Bound for Read-k Families of Functions

... when Y1 , . . . , Yr form a martingale, in which case Azuma inequality and its generalizations give bounds which are comparable to Chernoff bound. We consider in this paper another model of weak dependence. Assume that the variables Y1 , . . . , Yr can be factored as functions of independent random ...
PROBABILITY AS A NORMALIZED MEASURE “Probability is a
PROBABILITY AS A NORMALIZED MEASURE “Probability is a

Probability and Random Processes Measure
Probability and Random Processes Measure

Representing a distribution by stopping a Brownian Motion: Root`s
Representing a distribution by stopping a Brownian Motion: Root`s

A Continuous Analogue of the Upper Bound Theorem
A Continuous Analogue of the Upper Bound Theorem

A Continuous Analogue of the Upper Bound Theorem
A Continuous Analogue of the Upper Bound Theorem

A Continuous Analogue of the Upper Bound Theorem
A Continuous Analogue of the Upper Bound Theorem

ON THE NUMBER OF VERTICES OF RANDOM CONVEX POLYHEDRA 1 Introduction
ON THE NUMBER OF VERTICES OF RANDOM CONVEX POLYHEDRA 1 Introduction

Epidemic on Reed-frost Random Intersection Graph with Tunable
Epidemic on Reed-frost Random Intersection Graph with Tunable

Lecture 5: Hashing with real numbers and their big-data applications
Lecture 5: Hashing with real numbers and their big-data applications

DOC - MathsGeeks
DOC - MathsGeeks

Lecture 7: Chernoff`s Bound and Hoeffding`s Inequality 1 Developing
Lecture 7: Chernoff`s Bound and Hoeffding`s Inequality 1 Developing

Empirical Probability
Empirical Probability

On solutions of stochastic differential equations with parameters
On solutions of stochastic differential equations with parameters

Chapter 10 Idea of Probability Probability Model for Two Dice
Chapter 10 Idea of Probability Probability Model for Two Dice

AJP Journal
AJP Journal

On the Reliability of Clustering Stability in the Large A
On the Reliability of Clustering Stability in the Large A

< 1 ... 4 5 6 7 8 9 10 11 12 ... 20 >

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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