16 января в 18:00 Маргарита Грушанина (Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London) представит доклад "Dynamic Mixture of Finite Mixtures of Factor Analysers".
Mixtures of factor analysers represent a popular tool for finding structure in data. While in many applications the number of clusters and latent factors within clusters is held constant, some recent models automatically infer cluster and/or factor dimensionalities. This is done by employing nonparametric priors and allowing the number of clusters and factors to potentially be infinite. MCMC estimation is performed via adaptive algorithms, where parameters associated with the redundant factors are discarded. The current work contributes to the literature by allowing automatic inference on the number of clusters and cluster-specific factors while keeping both dimensions finite. For automatic inference on the cluster structure we employ the dynamic mixture of finite mixtures model with a prior on the number of mixture components. Automatic inference on cluster-specific factors is performed by assigning an exchangeable shrinkage process prior which can be interpreted as a generalized cumulative shrinkage process prior for the columns of the factor loading matrices.
Язык семинара — английский.
Мероприятие состоится онлайн. Для участия зарегистрируйтесь на Timepad (ссылка появится позже).
Если возникнут вопросы или предложения по проведению семинара, пишите: econ.seminar@eu.spb.ru.
Фото: Unsplash