👉 Fitted computing, also known as model fitting or parameter estimation, is a statistical process where the parameters of a mathematical model are determined by fitting the model to observed data. This involves adjusting the values of the model's parameters to minimize the discrepancy between the model's predictions and the actual data points. Techniques like maximum likelihood estimation, least squares, or Bayesian inference are commonly used to achieve this. The goal is to find the parameter values that best explain the observed data, thereby providing a more accurate representation of the underlying phenomenon being studied. This process is crucial in various fields, including machine learning, econometrics, and physics, where models are used to make predictions or understand complex systems.
