convergence parameter

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• Rate of convergence — In numerical analysis, the speed at which a convergent sequence approaches its limit is called the rate of convergence. Although strictly speaking, a limit does not give information about any finite first part of the sequence, this concept is of… …   Wikipedia

• Non-linear least squares — is the form of least squares analysis which is used to fit a set of m observations with a model that is non linear in n unknown parameters (m > n). It is used in some forms of non linear regression. The basis of the method is to… …   Wikipedia

• Mixture model — See also: Mixture distribution In statistics, a mixture model is a probabilistic model for representing the presence of sub populations within an overall population, without requiring that an observed data set should identify the sub population… …   Wikipedia

• CMA-ES — stands for Covariance Matrix Adaptation Evolution Strategy. Evolution strategies (ES) are stochastic, derivative free methods for numerical optimization of non linear or non convex continuous optimization problems. They belong to the class of… …   Wikipedia

• climate — /kluy mit/, n. 1. the composite or generally prevailing weather conditions of a region, as temperature, air pressure, humidity, precipitation, sunshine, cloudiness, and winds, throughout the year, averaged over a series of years. 2. a region or… …   Universalium

• Dirac delta function — Schematic representation of the Dirac delta function by a line surmounted by an arrow. The height of the arrow is usually used to specify the value of any multiplicative constant, which will give the area under the function. The other convention… …   Wikipedia

• Levenberg–Marquardt algorithm — In mathematics and computing, the Levenberg–Marquardt algorithm (LMA)[1] provides a numerical solution to the problem of minimizing a function, generally nonlinear, over a space of parameters of the function. These minimization problems arise… …   Wikipedia

• Consistent estimator — {T1, T2, T3, …} is a sequence of estimators for parameter θ0, the true value of which is 4. This sequence is consistent: the estimators are getting more and more concentrated near the true value θ0; at the same time, these estimators are biased.… …   Wikipedia

• Maximum likelihood — In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum likelihood estimation provides estimates for the model s… …   Wikipedia

• Ant colony optimization algorithms — Ant behavior was the inspiration for the metaheuristic optimization technique. In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be… …   Wikipedia

• Expectation-maximization algorithm — An expectation maximization (EM) algorithm is used in statistics for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. EM alternates between performing an… …   Wikipedia