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Examples of functions that are not entire include the square root, the logarithm, the trigonometric function tangent, and its inverse, arctan. For these functions the Taylor series do not converge if x is far from b. That is, the Taylor series diverges at x if the distance between x and b is larger than the radius of convergence. The Taylor ...
In probability theory, it is possible to approximate the moments of a function f of a random variable X using Taylor expansions, provided that f is sufficiently differentiable and that the moments of X are finite. A simulation-based alternative to this approximation is the application of Monte Carlo simulations.
In particular, the Taylor expansion holds in the form = + (), = = ()! (), where the remainder term R k is complex analytic. Methods of complex analysis provide some powerful results regarding Taylor expansions.
For large enough values of x, only the first few terms of this asymptotic expansion are needed to obtain a good approximation of erfc x (while for not too large values of x, the above Taylor expansion at 0 provides a very fast convergence).
In probability theory, the first-order second-moment (FOSM) method, also referenced as mean value first-order second-moment (MVFOSM) method, is a probabilistic method to determine the stochastic moments of a function with random input variables.
In mathematics, every analytic function can be used for defining a matrix function that maps square matrices with complex entries to square matrices of the same size.. This is used for defining the exponential of a matrix, which is involved in the closed-form solution of systems of linear differential equations.
The linear approximation of a function is the first order Taylor expansion around the point of interest. In the study of dynamical systems , linearization is a method for assessing the local stability of an equilibrium point of a system of nonlinear differential equations or discrete dynamical systems . [ 1 ]
This expansion is known as the multipole expansion of U AB. In order to derive this multipole expansion, we write r XY = r Y − r X , which is a vector pointing from X towards Y . Note that R A B + r B j + r j i + r i A = 0 r i j = R A B − r A i + r B j . {\displaystyle \mathbf {R} _{AB}+\mathbf {r} _{Bj}+\mathbf {r} _{ji}+\mathbf {r} _{iA ...