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Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes.Two events are independent, statistically independent, or stochastically independent [1] if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, does not affect the odds.
Given two independent events, if the first event can yield one of n equiprobable outcomes and another has one of m equiprobable outcomes then there are mn equiprobable outcomes of the joint event. This means that if log 2 ( n ) bits are needed to encode the first value and log 2 ( m ) to encode the second, one needs log 2 ( mn ) = log 2 ( m ...
The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density functions respectively.
This follows from the definition of independence in probability: the probabilities of two independent events happening, given a model, is the product of the probabilities. This is particularly important when the events are from independent and identically distributed random variables , such as independent observations or sampling with replacement .
In statistics, probability theory and information theory, pointwise mutual information (PMI), [1] or point mutual information, is a measure of association.It compares the probability of two events occurring together to what this probability would be if the events were independent.
Indeed, the propagator is often called a two-point correlation function for the free field. Since, by the postulates of quantum field theory, all observable operators commute with each other at spacelike separation, messages can no more be sent through these correlations than they can through any other EPR correlations; the correlations are in ...
In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.The general form of its probability density function is [2] [3] = ().
These bounds are not the tightest possible with general bivariates even when feasibility is guaranteed as shown in Boros et.al. [9] However, when the variables are pairwise independent (=), Ramachandra—Natarajan [10] showed that the Kounias-Hunter-Worsley [6] [7] [8] bound is tight by proving that the maximum probability of the union of ...