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Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations , probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms .
A random process is a sequence of random variables whose outcomes do not follow a deterministic pattern, but follow an evolution described by probability distributions. These and other constructs are extremely useful in probability theory and the various applications of randomness .
In probability theory and related fields, a stochastic (/ s t ə ˈ k æ s t ɪ k /) or random process is a mathematical object usually defined as a family of random variables in a probability space, where the index of the family often has the interpretation of time.
The Drunkard's Walk discusses the role of randomness in everyday events, and the cognitive biases that lead people to misinterpret random events and stochastic processes. The title refers to a certain type of random walk, a mathematical process in which one or more variables change value under a series of random steps.
In probability theory, a random measure is a measure-valued random element. [ 1 ] [ 2 ] Random measures are for example used in the theory of random processes , where they form many important point processes such as Poisson point processes and Cox processes .
The probability of an event A is written as (), [29] (), or (). [30] This mathematical definition of probability can extend to infinite sample spaces, and even uncountable sample spaces, using the concept of a measure.
[27] [28] Decades later Cramér referred to the 1930s as the "heroic period of mathematical probability theory". [28] In mathematics, the theory of stochastic processes is an important contribution to probability theory, [29] and continues to be an active topic of research for both theory and applications. [30] [31] [32]
In probability theory, an experiment or trial (see below) is any procedure that can be infinitely repeated and has a well-defined set of possible outcomes, known as the sample space. [1] An experiment is said to be random if it has more than one possible outcome, and deterministic if it has only one.