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Differentiation Of Integrals
In mathematics, the problem of differentiation of integrals is that of determining under what circumstances the mean value integral of a suitable function on a small neighbourhood of a point approximates the value of the function at that point. More formally, given a space ''X'' with a measure ''μ'' and a metric ''d'', one asks for what functions ''f'' : ''X'' → R does \lim_ \frac1 \int_ f(y) \, \mathrm \mu(y) = f(x) for all (or at least ''μ''-almost all) ''x'' ∈ ''X''? (Here, as in the rest of the article, ''B''''r''(''x'') denotes the open ball in ''X'' with ''d''-radius ''r'' and centre ''x''.) This is a natural question to ask, especially in view of the heuristic construction of the Riemann integral, in which it is almost implicit that ''f''(''x'') is a "good representative" for the values of ''f'' near ''x''. Theorems on the differentiation of integrals Lebesgue measure One result on the differentiation of integrals is the Lebesg ...
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Mathematics
Mathematics is a field of study that discovers and organizes methods, Mathematical theory, theories and theorems that are developed and Mathematical proof, proved for the needs of empirical sciences and mathematics itself. There are many areas of mathematics, which include number theory (the study of numbers), algebra (the study of formulas and related structures), geometry (the study of shapes and spaces that contain them), Mathematical analysis, analysis (the study of continuous changes), and set theory (presently used as a foundation for all mathematics). Mathematics involves the description and manipulation of mathematical object, abstract objects that consist of either abstraction (mathematics), abstractions from nature orin modern mathematicspurely abstract entities that are stipulated to have certain properties, called axioms. Mathematics uses pure reason to proof (mathematics), prove properties of objects, a ''proof'' consisting of a succession of applications of in ...
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Locally Integrable Function
In mathematics, a locally integrable function (sometimes also called locally summable function) is a function (mathematics), function which is integrable (so its integral is finite) on every compact subset of its domain of definition. The importance of such functions lies in the fact that their function space is similar to Lp space, spaces, but its members are not required to satisfy any growth restriction on their behavior at the boundary of their domain (at infinity if the domain is unbounded): in other words, locally integrable functions can grow arbitrarily fast at the domain boundary, but are still manageable in a way similar to ordinary integrable functions. Definition Standard definition .See for example and . Let be an open set in the Euclidean space \mathbb^n and be a Lebesgue measure, Lebesgue measurable function. If on is such that : \int_K , f , \, \mathrmx <+\infty, i.e. its Lebesgue integral is finite on all compact set, compact subsets of , then ...
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Measure Theory
In mathematics, the concept of a measure is a generalization and formalization of geometrical measures (length, area, volume) and other common notions, such as magnitude (mathematics), magnitude, mass, and probability of events. These seemingly distinct concepts have many similarities and can often be treated together in a single mathematical context. Measures are foundational in probability theory, integral, integration theory, and can be generalized to assume signed measure, negative values, as with electrical charge. Far-reaching generalizations (such as spectral measures and projection-valued measures) of measure are widely used in quantum physics and physics in general. The intuition behind this concept dates back to Ancient Greece, when Archimedes tried to calculate the area of a circle. But it was not until the late 19th and early 20th centuries that measure theory became a branch of mathematics. The foundations of modern measure theory were laid in the works of Émile B ...
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Differentiation Rules
This article is a summary of differentiation rules, that is, rules for computing the derivative of a function (mathematics), function in calculus. Elementary rules of differentiation Unless otherwise stated, all functions are functions of real numbers (\mathbb) that return real values, although, more generally, the formulas below apply wherever they are well defined, including the case of complex numbers (\mathbb). Constant term rule For any value of c, where c \in \mathbb, if f(x) is the constant function given by f(x) = c, then \frac = 0. Proof Let c \in \mathbb and f(x) = c. By the definition of the derivative: \begin f'(x) &= \lim_\frac \\ &= \lim_ \frac \\ &= \lim_ \frac \\ &= \lim_ 0 \\ &= 0. \end This computation shows that the derivative of any constant function is 0. Intuitive (geometric) explanation The derivative of the function at a point is the slope of the line tangent to the curve at the point. The slope of the constant function is 0, because the Tangent, tangen ...
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Convergence In Measure
Convergence in measure is either of two distinct mathematical concepts both of which generalize the concept of convergence in probability. Definitions Let f, f_n\ (n \in \mathbb N): X \to \mathbb R be measurable functions on a measure space (X, \Sigma, \mu). The sequence f_n is said to to f if for every \varepsilon > 0, \lim_ \mu(\) = 0, and to to f if for every \varepsilon>0 and every F \in \Sigma with \mu (F) 0 there exists ''F'' in the family such that \mu(G\setminus F)<\varepsilon. When \mu(X) < \infty , we may consider only one metric \rho_X, so the topology of convergence in finite measure is metrizable. If \mu is an arbitrary measure finite or not, then d(f,g) := \inf\limits_ \mu(\) + \delta still defines a metric that generates the global convergence in measure.Vladimir I. Bogachev, Measure Theory Vol. I, Springer Science & Business Media, 2007 Because this topology is generated by a ...
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Orthonormal Basis
In mathematics, particularly linear algebra, an orthonormal basis for an inner product space V with finite Dimension (linear algebra), dimension is a Basis (linear algebra), basis for V whose vectors are orthonormal, that is, they are all unit vectors and Orthogonality_(mathematics), orthogonal to each other. For example, the standard basis for a Euclidean space \R^n is an orthonormal basis, where the relevant inner product is the dot product of vectors. The Image (mathematics), image of the standard basis under a Rotation (mathematics), rotation or Reflection (mathematics), reflection (or any orthogonal transformation) is also orthonormal, and every orthonormal basis for \R^n arises in this fashion. An orthonormal basis can be derived from an orthogonal basis via Normalize (linear algebra), normalization. The choice of an origin (mathematics), origin and an orthonormal basis forms a coordinate frame known as an ''orthonormal frame''. For a general inner product space V, an orthono ...
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Countable Set
In mathematics, a set is countable if either it is finite or it can be made in one to one correspondence with the set of natural numbers. Equivalently, a set is ''countable'' if there exists an injective function from it into the natural numbers; this means that each element in the set may be associated to a unique natural number, or that the elements of the set can be counted one at a time, although the counting may never finish due to an infinite number of elements. In more technical terms, assuming the axiom of countable choice, a set is ''countable'' if its cardinality (the number of elements of the set) is not greater than that of the natural numbers. A countable set that is not finite is said to be countably infinite. The concept is attributed to Georg Cantor, who proved the existence of uncountable sets, that is, sets that are not countable; for example the set of the real numbers. A note on terminology Although the terms "countable" and "countably infinite" as def ...
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Covariance
In probability theory and statistics, covariance is a measure of the joint variability of two random variables. The sign of the covariance, therefore, shows the tendency in the linear relationship between the variables. If greater values of one variable mainly correspond with greater values of the other variable, and the same holds for lesser values (that is, the variables tend to show similar behavior), the covariance is positive. In the opposite case, when greater values of one variable mainly correspond to lesser values of the other (that is, the variables tend to show opposite behavior), the covariance is negative. The magnitude of the covariance is the geometric mean of the variances that are in common for the two random variables. The Pearson product-moment correlation coefficient, correlation coefficient normalizes the covariance by dividing by the geometric mean of the total variances for the two random variables. A distinction must be made between (1) the covariance of ...
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Vitali Covering Theorem
In mathematics, the Vitali covering lemma is a combinatorial geometry, combinatorial and geometric result commonly used in measure theory of Euclidean spaces. This lemma is an intermediate step, of independent interest, in the proof of the Vitali covering theorem. The covering theorem is credited to the Italy, Italian mathematician Giuseppe Vitali.. The theorem states that it is possible to cover, up to a Null set, Lebesgue-negligible set, a given subset ''E'' of R''d'' by a disjoint family extracted from a ''Vitali covering'' of ''E''. Vitali covering lemma There are two basic versions of the lemma, a finite version and an infinite version. Both lemmas can be proved in the general setting of a metric space, typically these results are applied to the special case of the Euclidean space \mathbb^d. In both theorems we will use the following notation: if B = B(x,r) is a Ball (mathematics), ball and c \geq 0 , we will write cB for the ball B(x,cr). Finite version Infinite ve ...
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Gaussian Measure
In mathematics, Gaussian measure is a Borel measure on finite-dimensional Euclidean space \mathbb^n, closely related to the normal distribution in statistics. There is also a generalization to infinite-dimensional spaces. Gaussian measures are named after the German mathematician Carl Friedrich Gauss. One reason why Gaussian measures are so ubiquitous in probability theory is the central limit theorem. Loosely speaking, it states that if a random variable X is obtained by summing a large number N of independent random variables with variance 1, then X has variance N and its law is approximately Gaussian. Definitions Let n \in N and let B_0(\mathbb^n) denote the completion of the Borel \sigma-algebra on \mathbb^n. Let \lambda^n : B_0(\mathbb^n) \to , +\infty/math> denote the usual n-dimensional Lebesgue measure. Then the standard Gaussian measure \gamma^n : B_0(\mathbb^n) \to , 1/math> is defined by \gamma^ (A) = \frac \int_ \exp \left( - \frac \left\, x \right\, _^ \right) ...
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Hilbert Space
In mathematics, a Hilbert space is a real number, real or complex number, complex inner product space that is also a complete metric space with respect to the metric induced by the inner product. It generalizes the notion of Euclidean space. The inner product allows lengths and angles to be defined. Furthermore, Complete metric space, completeness means that there are enough limit (mathematics), limits in the space to allow the techniques of calculus to be used. A Hilbert space is a special case of a Banach space. Hilbert spaces were studied beginning in the first decade of the 20th century by David Hilbert, Erhard Schmidt, and Frigyes Riesz. They are indispensable tools in the theories of partial differential equations, mathematical formulation of quantum mechanics, quantum mechanics, Fourier analysis (which includes applications to signal processing and heat transfer), and ergodic theory (which forms the mathematical underpinning of thermodynamics). John von Neumann coined the ...
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Separable Space
In mathematics, a topological space is called separable if it contains a countable, dense subset; that is, there exists a sequence ( x_n )_^ of elements of the space such that every nonempty open subset of the space contains at least one element of the sequence. Like the other axioms of countability, separability is a "limitation on size", not necessarily in terms of cardinality (though, in the presence of the Hausdorff axiom, this does turn out to be the case; see below) but in a more subtle topological sense. In particular, every continuous function on a separable space whose image is a subset of a Hausdorff space is determined by its values on the countable dense subset. Contrast separability with the related notion of second countability, which is in general stronger but equivalent on the class of metrizable spaces. First examples Any topological space that is itself finite or countably infinite is separable, for the whole space is a countable dense subset of itself. ...
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