MCMC for Imbalanced Categorical Data

Understand

Many modern applications collect highly imbalanced categorical data, with some categories relatively rare.

• Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty.
• However, posterior computation presents a fundamental barrier to routine use; a single class of algorithms does not work well in all settings and practitioners waste time trying different types of MCMC approaches.
• This article was motivated by an application to quantitative advertising in which we encountered extremely poor computational performance for common data augmentation MCMC algorithms but obtained excellent performance for adaptive Metropolis.