model misspecification

Power posteriors do not reliably learn the number of components in a finite mixture

Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. Data science folk wisdom tells us that a finite mixture model (FMM) with a prior on the number of components will fail to …

Finite mixture models do not reliably learn the number of components

Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. A common suggestion is to use a finite mixture model (FMM) with a prior on the number of components. Past work has shown the …

Edge-exchangeable graphs and sparsity

In NeurIPS 2016