Sep 19, 2017 · The algorithm that we call Lifted Marginal Filtering (LiMa) is inspired by Lifted Inference and combines techniques known from Computational ...

We presented the Lifted Marginal Filtering algorithm that sequentially estimates a probability distribution over situations in the Bayesian. Filtering framework ...

Lifted Marginal Filtering (LiMa) is inspired by Lifted Inference and combines techniques known from Computational State Space Models and Multiset Rewriting ...

The algorithm that we call Lifted Marginal Filtering (LiMa) is inspired by Lifted Inference and combines techniques known from Computational State Space Models ...

The algorithm that we call Lifted Marginal Filtering (LiMa) is inspired by Lifted Inference and combines techniques known from Computational State Space Models ...

The algorithm that we call Lifted Marginal Filtering (LiMa) is inspired by Lifted Inference and combines techniques known from Computational State Space Models ...

Lifted Marginal Filtering is a Bayesian Filtering algorithm that exploits these symmetries by representing symmetrical states by a single, parametric state ...

Bayesian Filtering for Lifted States. In this section, we present Lifted Marginal Filtering (LiMa), a BF algorithm that works directly on the lifted state ...

Sep 25, 2017 · LiMa: Sequential Lifted Marginal Filtering on Multiset State Descriptions. Max Schröder, Stefan Lüdtke, Sebastian Bader, Frank Krüger, Thomas ...

In this paper, we propose a method to retain the lifted representa- tion in LiMa. The method identifies groups of lifted multiset states that describe a ...