… such a lifted state representation can be found, in the case when the value distributions are
assumed to be Gaussian: In this case, we can fit a Gaussian … The lifted state is constructed by …

… depends ultimately on accurate filter implementations. We show that our Gaussian lifting
approach filters images more accurately and efficiently across many filter scales. Adaptive …

… Ideas from lifted inference have been applied to dynamic domains in the relational Kalman
filter [3], which is restricted to Gaussian filtering distributions and a linear transition model. …

Recently, Lifted Marginal Filtering (LiMa) has been proposed, an efficient exact Bayesian
filtering algorithm for stochastic systems consisting of multiple,(inter-) acting entities where the …

… If, additionally, assumption a is made, the desired means and covariances can be expressed
as functions of marginal moments in the nonlinear non-Gaussian subspaces, as will be …

… Our concept of Bayesian Filtering in state spaces described by … overview of our Lifted
Marginal Filtering approach. The next … for gaussian linear models, like the standard Kalman filter. …

… Gaussian scale mixtures (GSM), the marginal distributions of neighbor coefficients in the
lifting … transform retains the separable filtering, computation simplicity and filter design from the …

… Lifted Marginal Filtering In the following, we will briefly describe the Lifted Marginal Filtering
state representation and filtering … straightforward to handle are gaussian distributions, and …

… by a single lifted state. Technically, we propose a novel distance measure for lifted states
that … how such a single representative for a group of lifted states can be computed. We show …

… lifted (clustered) network. LBP has proved extremely fast at computing approximate marginal
… In this section, we present a unified review of Gaussian belief propagation (GaBP) and lifted …