felparametrizálása

Felparametrizálása is a Hungarian term that translates to "reparameterization" or "parameter re-estimation" in English. It refers to the process of adjusting the parameters of a mathematical model or system to better fit observed data or to achieve a desired outcome. This is a common practice in various fields, including statistics, machine learning, engineering, and scientific modeling.

The core idea behind felparametrizálása is that an initial model may have parameters that are not optimal. These parameters might have been set based on initial guesses, theoretical assumptions, or previous experiments. When new data becomes available, or when the performance of the model is unsatisfactory, felparametrizálása is employed to refine these parameters. This refinement typically involves an optimization process where a cost function or objective function is minimized or maximized. The cost function quantifies the difference between the model's predictions and the actual observed data, or it measures the model's performance against a specific goal.

Common techniques used in felparametrizálása include gradient descent, least squares estimation, maximum likelihood estimation, and Bayesian inference. The choice of method depends on the nature of the model, the data, and the specific problem being addressed. For instance, in machine learning, training a neural network is essentially a complex process of felparametrizálása, where the weights and biases of the network are adjusted iteratively to minimize prediction errors. In engineering, reparametrizing a control system might involve adjusting gain values to improve stability or responsiveness. The ultimate aim of felparametrizálása is to create a model that is more accurate, robust, and useful for its intended purpose.