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