Part of a series on statistics |

Probability theory |
---|

In mathematics, a **probability measure** is a real-valued function defined on a set of events in a probability space that satisfies measure properties such as *countable additivity*.^{ [1] } The difference between a probability measure and the more general notion of measure (which includes concepts like area or volume) is that a probability measure must assign value 1 to the entire probability space.

Intuitively, the additivity property says that the probability assigned to the union of two disjoint events by the measure should be the sum of the probabilities of the events, e.g. the value assigned to "1 or 2" in a throw of a die should be the sum of the values assigned to "1" and "2".

Probability measures have applications in diverse fields, from physics to finance and biology.

The requirements for a function *μ* to be a probability measure on a probability space are that:

*μ*must return results in the unit interval [0, 1], returning 0 for the empty set and 1 for the entire space.

*μ*must satisfy the*countable additivity*property that for all countable collections of pairwise disjoint sets:

For example, given three elements 1, 2 and 3 with probabilities 1/4, 1/4 and 1/2, the value assigned to {1, 3} is 1/4 + 1/2 = 3/4, as in the diagram on the right.

The conditional probability based on the intersection of events defined as:

satisfies the probability measure requirements so long as is not zero.^{ [2] }

Probability measures are distinct from the more general notion of fuzzy measures in which there is no requirement that the fuzzy values sum up to 1, and the additive property is replaced by an order relation based on set inclusion.

*Market measures* which assign probabilities to financial market spaces based on actual market movements are examples of probability measures which are of interest in mathematical finance, e.g. in the pricing of financial derivatives.^{ [5] } For instance, a risk-neutral measure is a probability measure which assumes that the current value of assets is the expected value of the future payoff taken with respect to that same risk neutral measure (i.e. calculated using the corresponding risk neutral density function), and discounted at the risk-free rate. If there is a unique probability measure that must be used to price assets in a market, then the market is called a complete market.^{ [6] }

Not all measures that intuitively represent chance or likelihood are probability measures. For instance, although the fundamental concept of a system in statistical mechanics is a measure space, such measures are not always probability measures.^{ [3] } In general, in statistical physics, if we consider sentences of the form "the probability of a system S assuming state A is p" the geometry of the system does not always lead to the definition of a probability measure under congruence, although it may do so in the case of systems with just one degree of freedom.^{ [4] }

Probability measures are also used in mathematical biology.^{ [7] } For instance, in comparative sequence analysis a probability measure may be defined for the likelihood that a variant may be permissible for an amino acid in a sequence.^{ [8] }

Ultrafilters can be understood as -valued probability measures, allowing for many intuitive proofs based upon measures. For instance, Hindman's Theorem can be proven from the further investigation of these measures, and their convolution in particular.

In mathematics, a **measure** on a set is a systematic way to assign a number to subsets of a set, intuitively interpreted as the size of the subset. Those sets which can be associated with such a number, we call **measurable sets**. In this sense, a measure is a generalization of the concepts of length, area, and volume. A particularly important example is the Lebesgue measure on a Euclidean space. This assigns the usual length, area, or volume to certain subsets of the given Euclidean space. For instance, the Lebesgue measure of an interval of real numbers is its usual length, but also assigns numbers to other kinds of sets in a way that is consistent with the lengths of intervals.

In mathematical analysis, a **null set** is a set that has **measure zero**. This can be characterized as a set that can be covered by a countable union of intervals of arbitrarily small total length.

**Probability theory** 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 these outcomes 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.

In measure theory, a property holds **almost everywhere** if, in a technical sense, the set for which the property holds takes up nearly all possibilities. The notion of "almost everywhere" is a companion notion to the concept of measure zero, and is analogous to the notion of *almost surely* in probability theory.

In mathematics, a **measurable cardinal** is a certain kind of large cardinal number. In order to define the concept, one introduces a two-valued measure on a cardinal κ, or more generally on any set. For a cardinal κ, it can be described as a subdivision of all of its subsets into large and small sets such that κ itself is large, ∅ and all singletons {*α*}, *α* ∈ *κ* are small, complements of small sets are large and vice versa. The intersection of fewer than *κ* large sets is again large.

In the mathematical field of measure theory, an **outer measure** or **exterior measure** is a function defined on all subsets of a given set with values in the extended real numbers satisfying some additional technical conditions. The theory of outer measures was first introduced by Constantin Carathéodory to provide an abstract basis for the theory of measurable sets and countably additive measures. Carathéodory's work on outer measures found many applications in measure-theoretic set theory, and was used in an essential way by Hausdorff to define a dimension-like metric invariant now called Hausdorff dimension. Outer measures are commonly used in the field of geometric measure theory.

In mathematics, the **ba space** of an algebra of sets is the Banach space consisting of all bounded and finitely additive signed measures on . The norm is defined as the variation, that is ,

In mathematics, the **total variation** identifies several slightly different concepts, related to the structure of the codomain of a function or a measure. For a real-valued continuous function *f*, defined on an interval [*a*, *b*] ⊂ **R**, its total variation on the interval of definition is a measure of the one-dimensional arclength of the curve with parametric equation *x* ↦ *f*(*x*), for *x* ∈ [*a*, *b*]. Functions whose total variation is finite are called **functions of bounded variation**.

In mathematics, additivity and sigma additivity of a function defined on subsets of a given set are abstractions of how intuitive properties of size of a set sum when considering multiple objects. Additivity is a weaker condition than σ-additivity; that is, σ-additivity implies additivity.

In mathematics, the **membership function** of a fuzzy set is a generalization of the indicator function for classical sets. In fuzzy logic, it represents the degree of truth as an extension of valuation. Degrees of truth are often confused with probabilities, although they are conceptually distinct, because fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition. Membership functions were introduced by Zadeh in the first paper on fuzzy sets (1965). Zadeh, in his theory of fuzzy sets, proposed using a membership function operating on the domain of all possible values.

In mathematics, the **Bernoulli scheme** or **Bernoulli shift** is a generalization of the Bernoulli process to more than two possible outcomes. Bernoulli schemes appear naturally in symbolic dynamics, and are thus important in the study of dynamical systems. Many important dynamical systems exhibit a repellor that is the product of the Cantor set and a smooth manifold, and the dynamics on the Cantor set are isomorphic to that of the Bernoulli shift. This is essentially the Markov partition. The term *shift* is in reference to the shift operator, which may be used to study Bernoulli schemes. The Ornstein isomorphism theorem shows that Bernoulli shifts are isomorphic when their entropy is equal.

In mathematics, more precisely in measure theory, an **atom** is a measurable set which has positive measure and contains no set of smaller positive measure. A measure which has no atoms is called **non-atomic** or **atomless**.

In mathematics, the **Bochner integral**, named for Salomon Bochner, extends the definition of Lebesgue integral to functions that take values in a Banach space, as the limit of integrals of simple functions.

In measure theory, **Carathéodory's extension theorem** states that any pre-measure defined on a given ring *R* of subsets of a given set *Ω* can be extended to a measure on the σ-algebra generated by *R*, and this extension is unique if the pre-measure is σ-finite. Consequently, any pre-measure on a ring containing all intervals of real numbers can be extended to the Borel algebra of the set of real numbers. This is an extremely powerful result of measure theory, and leads, for example, to the Lebesgue measure.

In mathematics, a positive measure *μ* defined on a *σ*-algebra Σ of subsets of a set *X* is called a finite measure if *μ*(*X*) is a finite real number, and a set *A* in Σ is of finite measure if *μ*(*A*) < ∞*.* The measure *μ* is called **σ-finite** if *X* is the countable union of measurable sets with finite measure. A set in a measure space is said to have ** σ-finite measure** if it is a countable union of measurable sets with finite measure. A measure being σ-finite is a weaker condition than being finite, i.e. all finite measures are σ-finite but there are (many) σ-finite measures that are not finite.

In mathematics, **ergodicity** expresses the idea that a point of a moving system, either a dynamical system or a stochastic process, will eventually visit all parts of the space that the system moves in, in a uniform and random sense. This implies that the average behavior of the system can be deduced from the trajectory of a "typical" point. Equivalently, a sufficiently large collection of random samples from a process can represent the average statistical properties of the entire process. Ergodicity is a property of the system; it is a statement that the system cannot be reduced or factored into smaller components. Ergodic theory is the study of systems possessing ergodicity.

The concept of an **abstract Wiener space** is a mathematical construction developed by Leonard Gross to understand the structure of Gaussian measures on infinite-dimensional spaces. The construction emphasizes the fundamental role played by the Cameron–Martin space. The classical Wiener space is the prototypical example.

In mathematics, a **vector measure** is a function defined on a family of sets and taking vector values satisfying certain properties. It is a generalization of the concept of finite measure, which takes nonnegative real values only.

In mathematics, a **content** is a set function that is like a measure, but a content must only be finitely additive, whereas a measure must be countably additive. A content is a real function defined on a collection of subsets such that

In mathematics, the integral of a non-negative function of a single variable can be regarded, in the simplest case, as the area between the graph of that function and the *x*-axis. The **Lebesgue integral** extends the integral to a larger class of functions. It also extends the domains on which these functions can be defined.

- ↑
*An introduction to measure-theoretic probability*by George G. Roussas 2004 ISBN 0-12-599022-7 page 47 - ↑
*Probability, Random Processes, and Ergodic Properties*by Robert M. Gray 2009 ISBN 1-4419-1089-1 page 163 - 1 2
*A course in mathematics for students of physics, Volume 2*by Paul Bamberg, Shlomo Sternberg 1991 ISBN 0-521-40650-1 page 802 - 1 2
*The concept of probability in statistical physics*by Yair M. Guttmann 1999 ISBN 0-521-62128-3 page 149 - ↑
*Quantitative methods in derivatives pricing*by Domingo Tavella 2002 ISBN 0-471-39447-5 page 11 - ↑
*Irreversible decisions under uncertainty*by Svetlana I. Boyarchenko, Serge Levendorskiĭ 2007 ISBN 3-540-73745-6 page 11 - ↑
*Mathematical Methods in Biology*by J. David Logan, William R. Wolesensky 2009 ISBN 0-470-52587-8 page 195 - ↑
*Discovering biomolecular mechanisms with computational biology*by Frank Eisenhaber 2006 ISBN 0-387-34527-2 page 127

- Billingsley, Patrick (1995).
*Probability and Measure*. John Wiley. ISBN 0-471-00710-2. - Ash, Robert B.; Doléans-Dade, Catherine A. (1999).
*Probability & Measure Theory*. Academic Press. ISBN 0-12-065202-1.

- Media related to Probability measure at Wikimedia Commons

This page is based on this Wikipedia article

Text is available under the CC BY-SA 4.0 license; additional terms may apply.

Images, videos and audio are available under their respective licenses.

Text is available under the CC BY-SA 4.0 license; additional terms may apply.

Images, videos and audio are available under their respective licenses.