Multinomial Logit

Multinomial Logit (MNL) models are statistical tools used to predict the probability of choosing one option from a set of alternatives, finding wide application in fields like economics, marketing, and reinforcement learning. Current research focuses on improving the efficiency and scalability of MNL estimation, particularly for large datasets and complex scenarios, employing techniques like Expectation-Maximization (EM) algorithms, Nyström approximation for kernel methods, and novel neural network architectures such as Transformer Choice Nets. These advancements enhance the accuracy and applicability of MNL models in diverse settings, leading to improved decision-making in areas like assortment optimization and recommendation systems.