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Event (probability theory) – In statistics and probability theory, set of outcomes to which a probability is assigned; Sample space – Set of all possible outcomes or results of a statistical trial or experiment; Probability distribution – Mathematical function for the probability a given outcome occurs in an experiment
The probability that at least one of the events will occur is equal to one. [4] For example, there are theoretically only two possibilities for flipping a coin. Flipping a head and flipping a tail are collectively exhaustive events, and there is a probability of one of flipping either a head or a tail.
In probability theory, the joint probability distribution is the probability distribution of all possible pairs of outputs of two random variables that are defined on the same probability space. The joint distribution can just as well be considered for any given number of random variables.
Random variables are usually written in upper case Roman letters, such as or and so on. Random variables, in this context, usually refer to something in words, such as "the height of a subject" for a continuous variable, or "the number of cars in the school car park" for a discrete variable, or "the colour of the next bicycle" for a categorical variable.
Most generally, any X i and X j in the process are simply two from a set of random variables indexed by {1, 2, ..., n}, the finite cases, or by {1, 2, 3, ...}, the infinite cases. One experiment with only two possible outcomes, often referred to as "success" and "failure", usually encoded as 1 and 0, can be modeled as a Bernoulli distribution. [1]
In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the " amount of information " (in units such as shannons ( bits ), nats or hartleys ) obtained about one random variable by observing the other random ...
Suppose (X n) is a sequence of random variables with Pr(X n = 0) = 1/n 2 for each n. The probability that X n = 0 occurs for infinitely many n is equivalent to the probability of the intersection of infinitely many [X n = 0] events. The intersection of infinitely many such events is a set of outcomes common to all of them.
A measurable function on a probability space, often real-valued. The distribution function of a random variable gives the probability of the different values of the variable. The mean and variance of a random variable can also be derived. See also discrete random variable and continuous random variable. randomized block design range