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Standard probability space information


In probability theory, a standard probability space, also called Lebesgue–Rokhlin probability space or just Lebesgue space (the latter term is ambiguous) is a probability space satisfying certain assumptions introduced by Vladimir Rokhlin in 1940. Informally, it is a probability space consisting of an interval and/or a finite or countable number of atoms.

The theory of standard probability spaces was started by von Neumann in 1932 and shaped by Vladimir Rokhlin in 1940. Rokhlin showed that the unit interval endowed with the Lebesgue measure has important advantages over general probability spaces, yet can be effectively substituted for many of these in probability theory. The dimension of the unit interval is not an obstacle, as was clear already to Norbert Wiener. He constructed the Wiener process (also called Brownian motion) in the form of a measurable map from the unit interval to the space of continuous functions.

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Standard probability space

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In probability theory, a standard probability space, also called Lebesgue–Rokhlin probability space or just Lebesgue space (the latter term is ambiguous)...

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Probability space

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In probability theory, a probability space or a probability triple ( Ω , F , P ) {\displaystyle (\Omega ,{\mathcal {F}},P)} is a mathematical construct...

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Standard Borel space

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family of standard Borel spaces are standard. Every complete probability measure on a standard Borel space turns it into a standard probability space. Theorem...

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Lebesgue space

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Lebesgue space may refer to: Lp space, a special Banach space of functions (or rather, equivalence classes of functions) Standard probability space, a non-pathological...

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Sample space

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In probability theory, the sample space (also called sample description space, possibility space, or outcome space) of an experiment or random trial is...

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List of probability topics

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inequality Probability theory Probability space Sample space Standard probability space Random element Random compact set Dynkin system Probability axioms...

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Law of total probability

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In probability theory, the law (or formula) of total probability is a fundamental rule relating marginal probabilities to conditional probabilities. It...

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List of statistics articles

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applications Standard deviation Standard error Standard normal deviate Standard normal table Standard probability space Standard score Standardized coefficient...

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Empirical probability

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not of a theoretical sample space but of an actual experiment. More generally, empirical probability estimates probabilities from experience and observation...

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Borel set

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Borel. See analytic set. Every probability measure on a standard Borel space turns it into a standard probability space. An example of a subset of the...

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Probability distribution

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random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the...

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Probability axioms

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The standard probability axioms are the foundations of probability theory introduced by Russian mathematician Andrey Kolmogorov in 1933. These axioms remain...

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Probability mass function

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In probability and statistics, a probability mass function (sometimes called probability function or frequency function) is a function that gives the...

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Outline of probability

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measure theory) Sample spaces, σ-algebras and probability measures Probability space Sample space Standard probability space Random element Random compact...

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Bernoulli scheme

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because the countable direct product of a standard probability space is again a standard probability space. As a further generalization, one may replace...

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Probability theory

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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...

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Probability density function

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In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose...

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Log probability

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probabilities means representing probabilities on a logarithmic scale ( − inf , 0 ] {\displaystyle (-\inf ,0]} , instead of the standard [ 0 , 1 ] {\displaystyle...

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Probability amplitude

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function as the probability amplitude is a pillar of the Copenhagen interpretation of quantum mechanics. In fact, the properties of the space of wave functions...

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Vladimir Abramovich Rokhlin

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Rokhlin partitions. He introduced the notion of standard probability space, and characterised such spaces up to isomorphism mod 0. He also proved the famous...

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Normal distribution

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In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued...

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Binomial distribution

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In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes...

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Prior probability

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A prior probability distribution of an uncertain quantity, often simply called the prior, is its assumed probability distribution before some evidence...

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Joint probability distribution

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variables that are defined on the same probability space, the joint probability distribution is the corresponding probability distribution on all possible pairs...

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Convergence of random variables

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In probability theory, there exist several different notions of convergence of sequences of random variables, including convergence in probability, convergence...

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Notation in probability and statistics

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Probability theory and statistics have some commonly used conventions, in addition to standard mathematical notation and mathematical symbols. Random variables...

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