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.
Common techniques used in felparametrizálása include gradient descent, least squares estimation, maximum likelihood estimation, and Bayesian