Parameter Estimation

Parameter estimation uses observed data to infer the unknown settings that govern a model, enabling prediction, scientific explanation, and control. Approaches range from maximum likelihood and Bayesian methods to simulation-based inference, gradient or evolutionary optimization, and physics-informed neural networks that embed known equations. Researchers apply these tools to noisy, incomplete observations in settings such as dynamical systems, medical models, imaging, and scientific simulations, often estimating uncertainty as well as a best-fit value. A central challenge is that good predictions do not guarantee that parameters are uniquely or accurately identified, especially with sparse data, model mismatch, or costly simulations.