Maximum Likelihood Estimation (MLE) is a statistical parameter estimation method that identifies the parameter values of a model that maximize the probability (likelihood) of observing the given data, effectively finding the most plausible model configuration to explain observed crowd behavior. In WiFi/CSI sensing and crowd modeling research, MLE matters because it provides a principled, mathematically rigorous framework for fitting simulation or signal models to real-world measurements, enabling objective comparison and calibration of competing algorithms or models against empirical data. Key variants relevant to the field include penalized or regularized maximum likelihood approaches, which add constraints to prevent overfitting when data are limited, and expectation-maximization (EM) algorithms, which extend MLE to settings involving latent or unobserved variables such as hidden crowd states or unresolved signal components.
Source Papers
- Data-driven Crowd Modeling Techniques: A Survey ↗ — Data-driven Crowd Modeling Techniques: A Survey
- Enabling ISAC on Low-Cost Devices via Spatial-Channel Estimation With a Single-RF Chain ↗ — Enabling ISAC on Low-Cost Devices via Spatial-Channel Estima
- Parameter estimation and comparative evaluation of crowd simulations ↗ — Parameter estimation and comparative evaluation of crowd sim
- Social force models for pedestrian traffic – state of the art ↗ — Social force models for pedestrian traffic – state of the ar