Parameter Estimation
Parameter estimation is a statistical and computational technique used to infer the values of unknown parameters in a mathematical model based on observed data. It involves using algorithms and methods to find parameter values that best fit the data, often by minimizing error or maximizing likelihood. This concept is fundamental in fields like machine learning, econometrics, and engineering for building predictive models and understanding underlying systems.
Developers should learn parameter estimation when working on data-driven projects, such as training machine learning models (e.g., linear regression, neural networks), performing statistical analysis, or optimizing simulations. It is essential for tasks like model calibration, hypothesis testing, and making predictions from noisy or incomplete data, enabling more accurate and reliable outcomes in applications ranging from finance to scientific research.