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  2. Independence (probability theory) - Wikipedia

    en.wikipedia.org/wiki/Independence_(probability...

    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.

  3. Independent and identically distributed random variables

    en.wikipedia.org/wiki/Independent_and...

    A chart showing a uniform distribution. In probability theory and statistics, a collection of random variables is independent and identically distributed (i.i.d., iid, or IID) if each random variable has the same probability distribution as the others and all are mutually independent. [1]

  4. Conditional independence - Wikipedia

    en.wikipedia.org/wiki/Conditional_independence

    Two random variables and are conditionally independent given a random variable if they are independent given σ(W): the σ-algebra generated by . This is commonly written: This is commonly written: X ⊥ ⊥ Y ∣ W {\displaystyle X\perp \!\!\!\perp Y\mid W} or

  5. Distribution of the product of two random variables - Wikipedia

    en.wikipedia.org/wiki/Distribution_of_the...

    A product distribution is a probability distribution constructed as the distribution of the product of random variables having two other known distributions. Given two statistically independent random variables X and Y, the distribution of the random variable Z that is formed as the product = is a product distribution.

  6. Joint probability distribution - Wikipedia

    en.wikipedia.org/wiki/Joint_probability_distribution

    When two or more random variables are defined on a probability space, it is useful to describe how they vary together; that is, it is useful to measure the relationship between the variables. A common measure of the relationship between two random variables is the covariance.

  7. Pairwise independence - Wikipedia

    en.wikipedia.org/wiki/Pairwise_independence

    In probability theory, a pairwise independent collection of random variables is a set of random variables any two of which are independent. [1] Any collection of mutually independent random variables is pairwise independent, but some pairwise independent collections are not mutually independent.

  8. Probability space - Wikipedia

    en.wikipedia.org/wiki/Probability_space

    Two random variables, X and Y, are said to be independent if any event defined in terms of X is independent of any event defined in terms of Y. Formally, they generate independent σ-algebras, where two σ-algebras G and H, which are subsets of F are said to be independent if any element of G is independent of any element of H.

  9. Random variable - Wikipedia

    en.wikipedia.org/wiki/Random_variable

    The probability distribution of the sum of two independent random variables is the convolution of each of their distributions. Probability distributions are not a vector space —they are not closed under linear combinations , as these do not preserve non-negativity or total integral 1—but they are closed under convex combination , thus ...