Contents
What problem does the EM algorithm solve?
EM solves the parameter estimation problem by transferring the task of maximizing incomplete-data likelihood (difficult) to maximizing complete-data likelihood (easier) in some small steps.
How does EM imputation work?
It uses the E-M Algorithm, which stands for Expectation-Maximization. It is an iterative procedure in which it uses other variables to impute a value (Expectation), then checks whether that is the value most likely (Maximization). If not, it re-imputes a more likely value.
How to choose the right numerical optimization algorithm?
Maybe you should get yourself an introductory book about numerical optimization. You will need to take into account your function in order to decide for the algorithm. Among the algorithms you mention, important differences are whether the Jacobian or Hessian is needed or only the function itself.
What kind of problems are in numerical analysis?
These problems are of type linear and non linear regression. I want to check if the method under study is an improvement over generalized least squares. I am including multiple factors like accuracy, computational efficiency, robustness, sensitivity in my study under different combinations of stochastic models.
Which is the best algorithm to minimize a function?
Minimize a function using the BFGS algorithm. Minimize a function with nonlinear conjugate gradient algorithm. Minimize the function f using the Newton-CG method. Minimize a function using modified Powell’s method.
How to choose the right optimization algorithm [ cross validated ]?
Reading the docs at http://docs.scipy.org/doc/scipy/reference/optimize.html I see that there are several algorithms that do the same thing, i.e. find the minimum. How do I know which one I should choose? Minimize a function using the downhill simplex algorithm. Minimize a function using the BFGS algorithm.