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 tem-
plate 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 dferent 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 ex-
ploiting symmetries on the level of variable
assignments and relate it to novel results in
lifted MCMC.
Zusätzliche Informationen:
Paper pres. at the 2nd International Workshop on Statistical Relational AI : held at the Uncertanty in Artificial Intelligence Conference (UAI 2012), Catalina Island, Calif., August 18, 2012
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