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- W2621439971 abstract "The general consensus seems to be that liftedinference is concerned with exploiting modelsymmetries and grouping indistinguishableobjects at inference time. Since first-orderprobabilistic formalisms are essentially tem-plate languages providing a more compactrepresentation of a corresponding groundmodel, lifted inference tends to work especially well in these models. We show that thenotion of indistinguishability manifests itselfon several dferent levels {the level of constants, the level of ground atoms (variables),the level of formulas (features), and the levelof assignments (possible worlds). We discussexisting work in the MCMC literature on ex-ploiting symmetries on the level of variableassignments and relate it to novel results inlifted MCMC." @default.
- W2621439971 created "2017-06-15" @default.
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- W2621439971 date "2012-01-01" @default.
- W2621439971 modified "2023-09-27" @default.
- W2621439971 title "Lifted Probabilistic Inference: An MCMC Perspective" @default.
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