Log Likelihood

Log likelihood, the probability of observing data given a specific model, is a fundamental concept in statistical inference and machine learning, serving as a cornerstone for parameter estimation and model comparison. Current research focuses on improving log-likelihood estimation in various contexts, including developing more efficient algorithms for Bayesian inference (e.g., using neural network surrogates or online methods) and addressing challenges in high-dimensional data and complex models (e.g., mixtures of Plackett-Luce models, Gaussian processes). Accurate and efficient log-likelihood calculations are crucial for diverse applications, ranging from anomaly detection and causal discovery to large language model evaluation and generative modeling, impacting the reliability and performance of numerous machine learning systems.