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That is, the joint distribution is equal to the product of the marginal distributions. [2] Unless it is not clear in context, in practice the modifier "mutual" is usually dropped so that independence means mutual independence. A statement such as " X, Y, Z are independent random variables" means that X, Y, Z are mutually independent.
The i.i.d. assumption is also used in the central limit theorem, which states that the probability distribution of the sum (or average) of i.i.d. variables with finite variance approaches a normal distribution. [4] The i.i.d. assumption frequently arises in the context of sequences of random variables. Then, "independent and identically ...
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.
The Global Markov property is stronger than the Local Markov property, which in turn is stronger than the Pairwise one. [4] However, the above three Markov properties are equivalent for positive distributions [5] (those that assign only nonzero probabilities to the associated variables).
If X 1 and X 2 are independent exponential random variables with rate μ 1 and μ 2 respectively, then min(X 1, X 2) is an exponential random variable with rate μ = μ 1 + μ 2. Similarly, distributions for which the maximum value of several independent random variables is a member of the same family of distribution include: Bernoulli ...
The joint distribution encodes the marginal distributions, i.e. the distributions of each of the individual random variables and 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).
In statistics, an exchangeable sequence of random variables (also sometimes interchangeable) [1] is a sequence X 1, X 2, X 3, ... (which may be finitely or infinitely long) whose joint probability distribution does not change when the positions in the sequence in which finitely many of them appear are altered.
The pair distribution function describes the distribution of distances between pairs of particles contained within a given volume. [1] Mathematically, if a and b are two particles, the pair distribution function of b with respect to a, denoted by () is the probability of finding the particle b at distance from a, with a taken as the origin of coordinates.