## Glossary

Here we list some of the terminology, including acronyms, you will encounter when using lace.

**view**: A cluster of columns within a state.**category**: A cluster of rows within a view.**component model**: The probability distribution defining the model of a specific category in a column.**state**: what we call a lace posterior sample. Each state represents the current configuration (or state) of an independent markov chain. We aggregate over states to achieve estimate of likelihoods and uncertainties.**metadata**: A set of files from which an`Engine`

may be loaded**prior**: A probability distribution that describe how likely certain hypotheses (model parameters) are before we observe any data.**hyperprior**: A prior distribution on prior. Allows us to admit a larger amount of initial uncertainty and permit a broader set of hypotheses.**empirical hyperprior**: A hyperprior with parameters derived from data.**CRP**: Chinese restaurant process**DPMM**: Dirichlet process mixture model**PCC**: Probabilistic cross-categorization