Skip to main content
Article thumbnail
Location of Repository

Lifted Probabilistic Inference: An MCMC Perspective

By Mathias Niepert and Universität Mannheim

Abstract

The general consensus seems to be that lifted inference is concerned with exploiting model symmetries and grouping indistinguishable objects at inference time. Since first-order probabilistic formalisms are essentially template languages providing a more compact representation of a corresponding ground model, lifted inference tends to work especially well in these models. We show that the notion of indistinguishability manifests itself on several different levels – the level of constants, the level of ground atoms (variables), the level of formulas (features), and the level of assignments (possible worlds). We discuss existing work in the MCMC literature on exploiting symmetries on the level of variable assignments and relate it to novel results in lifted MCMC.

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.2100
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://matlog.net/pdfs/starai2... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.