Theory of probability in the context of Probability space


Theory of probability in the context of Probability space

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⭐ Core Definition: Theory of probability

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. Typically these axioms formalise probability in terms of a probability space, which assigns a measure taking values between 0 and 1, termed the probability measure, to a set of outcomes called the sample space. Any specified subset of the sample space is called an event.

Central subjects in probability theory include discrete and continuous random variables, probability distributions, and stochastic processes (which provide mathematical abstractions of non-deterministic or uncertain processes or measured quantities that may either be single occurrences or evolve over time in a random fashion).Although it is not possible to perfectly predict random events, much can be said about their behavior. Two major results in probability theory describing such behaviour are the law of large numbers and the central limit theorem.

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Theory of probability in the context of Hans Reichenbach

Hans Reichenbach (/ˈrxənbɑːx/; German: [ˈʁaɪçənbax]; September 26, 1891 – April 9, 1953) was a leading philosopher of science, educator, and proponent of logical empiricism. He founded the Gesellschaft für empirische Philosophie (Society for Empirical Philosophy) in Berlin in 1928, also known as the "Berlin Circle". Carl Gustav Hempel, Richard von Mises, David Hilbert and Kurt Grelling all became members of the Berlin Circle.

In 1930, Reichenbach and Rudolf Carnap became editors of the journal Erkenntnis. He also made lasting contributions to the study of empiricism based on a theory of probability; the logic and the philosophy of mathematics; space, time, and relativity theory; analysis of probabilistic reasoning; and quantum mechanics. In 1951, he authored The Rise of Scientific Philosophy, his most popular book.

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Theory of probability in the context of Glivenko–Cantelli theorem

In the theory of probability, the Glivenko–Cantelli theorem (sometimes referred to as the fundamental theorem of statistics), named after Valery Ivanovich Glivenko and Francesco Paolo Cantelli, describes the asymptotic behaviour of the empirical distribution function as the number of independent and identically distributed observations grows. Specifically, the empirical distribution function converges uniformly to the true distribution function almost surely.

The uniform convergence of more general empirical measures becomes an important property of the Glivenko–Cantelli classes of functions or sets. The Glivenko–Cantelli classes arise in Vapnik–Chervonenkis theory, with applications to machine learning. Applications can be found in econometrics making use of M-estimators.

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