Gibbs Sampling in Open-Universe Stochastic Languages

Understand

Languages for open-universe probabilistic models (OUPMs) can represent situations with an unknown number of objects and iden- tity uncertainty.

• While such cases arise in a wide range of important real-world appli- cations, existing general purpose inference methods for OUPMs are far less efficient than those available for more restricted lan- guages and model classes.
• This paper goes some way to remedying this deficit by in- troducing, and proving correct, a generaliza- tion of Gibbs sampling to partial worlds with possibly varying model structure.
• Our ap- proach draws on and extends previous generic OUPM inference methods, as well as aux- iliary variable samplers for nonparametric mixture models.