Search results
Results from the WOW.Com Content Network
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 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.
The Taylor expansion would be: + where / denotes the partial derivative of f k with respect to the i-th variable, evaluated at the mean value of all components of vector x. Or in matrix notation , f ≈ f 0 + J x {\displaystyle \mathrm {f} \approx \mathrm {f} ^{0}+\mathrm {J} \mathrm {x} \,} where J is the Jacobian matrix .
That is, the Taylor series diverges at x if the distance between x and b is larger than the radius of convergence. The Taylor series can be used to calculate the value of an entire function at every point, if the value of the function, and of all of its derivatives, are known at a single point. Uses of the Taylor series for analytic functions ...
By the Taylor expansion, [() ... as , so with probability converging to one, is finite for large n. Moreover, if ^ and ^ are estimates ...
This is a list of probability topics. It overlaps with the (alphabetical) ... Taylor expansions for the moments of functions of random variables; Moment problem.
Probability, thermodynamics, digital communications: ... An expansion, [6] which converges more rapidly for all real values of x than a Taylor expansion, ...
Method of moments (probability theory) Method of moments (statistics) Moment measure; ... Taylor expansions for the moments of functions of random variables; V.