Likelihood Ratio

Likelihood ratio (LR) methods assess the relative plausibility of different hypotheses or models by comparing their likelihoods given observed data. Current research focuses on improving LR estimation, particularly for high-dimensional data and in scenarios with limited or noisy data, employing techniques like neural networks, kernel methods, and diffusion models to approximate likelihoods efficiently. These advancements are impacting diverse fields, including anomaly detection (e.g., out-of-distribution detection in language models and image captioning), model training (e.g., improving neural network optimization), and statistical inference (e.g., constructing confidence intervals for complex models). The improved accuracy and efficiency of LR methods are driving progress in these areas.