Add to ORKGLifted belief propagation (LBP) can be extremely fast at computing approximate marginal probability distributions over single variables and neighboring ones in the underlying graphical model. It does, however, not prescribe a way to compute joint distributions over pairs, triples or k-tuples of distant random variables. In this paper, we present an algorithm, called conditioned LBP, for approximating these distributions. Essentially, we select variables one at a time for conditioning, running lifted belief propagation after each selection. This naive solution, however, recomputes the lifted network in each step from scratch, therefore often canceling the benefits of lifted inference. We show how to avoid this by efficiently computing the li...
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combine...
Belief propagation (BP) on cyclic graphs is an efficient algorithm for computing approximate margina...
Lifted inference algorithms for probabilistic first-order logic frameworks such as Markov logic netw...
Lifted message passing approaches can be extremely fast at computing approximate marginal probabilit...
Lifted inference, handling whole sets of indistinguishable objects together, is critical to the effe...
Lifted inference, handling whole sets of indistinguishable objects together, is critical to the eff...
Many AI applications need to explicitly represent relational structure as well as handle uncertainty...
We propose an approach to lifted approximate inference for first-order probabilistic models, such as...
By handling whole sets of indistinguishable objects together, lifted belief propagation approaches h...
By handling whole sets of indistinguishable objects together, lifted belief propagation approaches h...
Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, a...
Belief propagation over pairwise-connected Markov random fields has become a widely used approach, a...
Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, a...
Markov logic networks (MLNs) are a popular statistical relational learning formalism that combine Ma...
Judging by the increasing impact of machine learning on large-scale data analysis in the last decade...
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combine...
Belief propagation (BP) on cyclic graphs is an efficient algorithm for computing approximate margina...
Lifted inference algorithms for probabilistic first-order logic frameworks such as Markov logic netw...
Lifted message passing approaches can be extremely fast at computing approximate marginal probabilit...
Lifted inference, handling whole sets of indistinguishable objects together, is critical to the effe...
Lifted inference, handling whole sets of indistinguishable objects together, is critical to the eff...
Many AI applications need to explicitly represent relational structure as well as handle uncertainty...
We propose an approach to lifted approximate inference for first-order probabilistic models, such as...
By handling whole sets of indistinguishable objects together, lifted belief propagation approaches h...
By handling whole sets of indistinguishable objects together, lifted belief propagation approaches h...
Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, a...
Belief propagation over pairwise-connected Markov random fields has become a widely used approach, a...
Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, a...
Markov logic networks (MLNs) are a popular statistical relational learning formalism that combine Ma...
Judging by the increasing impact of machine learning on large-scale data analysis in the last decade...
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combine...
Belief propagation (BP) on cyclic graphs is an efficient algorithm for computing approximate margina...
Lifted inference algorithms for probabilistic first-order logic frameworks such as Markov logic netw...