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Download as PDF; Printable version; In other projects ... In mathematics the Markov theorem gives necessary and sufficient conditions for two braids to have closures ...
The theorem was named after Carl Friedrich Gauss and Andrey Markov, although Gauss' work significantly predates Markov's. [3] But while Gauss derived the result under the assumption of independence and normality, Markov reduced the assumptions to the form stated above. [4] A further generalization to non-spherical errors was given by Alexander ...
Download as PDF; Printable version ... In mathematics, the Markov spectrum, devised by Andrey Markov, is a ... Markov's theorem and 100 years of the uniqueness ...
All the Markov numbers on the regions adjacent to 2's region are odd-indexed Pell numbers (or numbers n such that 2n 2 − 1 is a square, OEIS: A001653), and all the Markov numbers on the regions adjacent to 1's region are odd-indexed Fibonacci numbers (OEIS: A001519). Thus, there are infinitely many Markov triples of the form
The term Markov assumption is used to describe a model where the Markov property is assumed to hold, such as a hidden Markov model. A Markov random field extends this property to two or more dimensions or to random variables defined for an interconnected network of items. [1] An example of a model for such a field is the Ising model.
Gauss–Lucas theorem (complex analysis) Gauss–Markov theorem ; Gauss–Wantzel theorem ; Gelfand–Mazur theorem (Banach algebra) Gelfand–Naimark theorem (functional analysis) Gelfond–Schneider theorem (transcendental number theory) Gell-Mann and Low theorem (quantum field theory) Geometric mean theorem
Gauss–Markov theorem, the statement that the least-squares estimators in certain linear models are the best linear unbiased estimators Gaussian correlation inequality Gaussian isoperimetric inequality
In probability theory, Markov's inequality gives an upper bound on the probability that a non-negative random variable is greater than or equal to some positive constant. Markov's inequality is tight in the sense that for each chosen positive constant, there exists a random variable such that the inequality is in fact an equality.