Likelihood Estimation

Likelihood estimation, a core statistical problem, aims to determine the parameters of a probability model that best explain observed data. Current research focuses on improving the efficiency and accuracy of likelihood estimation across diverse applications, including generative modeling, psychometrics (e.g., using novel estimators like random pairing maximum likelihood), and medical image analysis (e.g., leveraging k-space information in MRI). These advancements are crucial for enhancing the reliability and interpretability of models in various scientific fields and practical applications, ranging from improving the fairness and stability of generative models to enabling more accurate medical diagnoses.