It is common to make the additional hypothesis that the ordinary least squares method should be used to minimize the " residuals ".
22.
The idea behind the Quasi-Newton Least Squares Method is to build up an approximate Jacobian based on known input-output pairs of the function.
23.
Least squares applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is called non-linear least squares.
24.
The idea behind the Quasi-Newton Inverse Least Squares Method is to build up an approximate Jacobian based on known input-output pairs of the function.
25.
The mathematician John Von Neumann suggested one of the first rather imperfect pseudorandom number generators _ a simple procedure, or algorithm, called the middle square method.
26.
Using lists of " truly random " random numbers was extremely slow, but von Neumann developed a way to calculate pseudorandom numbers, using the middle-square method.
27.
In ( Tiwari et al . 2001 ), this method has been applied to a grid free framework with the help of the weighted least squares method.
28.
Also, by iteratively applying local quadratic approximation to the likelihood ( through the Fisher information ), the least-squares method may be used to fit a generalized linear model.
29.
Other methods that can be used are the Column Updating Method, the Inverse Column Updating Method, the Quasi-Newton Least Squares Method and the Quasi-Newton Inverse Least Squares Method.
30.
Using more than four involves an over-determined system of equations with no unique solution; such a system can be solved by a least-squares or weighted least squares method.
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