8 papers
Lifted Causal Inference
Malte Luttermann, Tanya Braun, Ralf Möller +1
Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering whil…
Inducing Comparability of Factorised Probability Distributions
Jan Speller, Malte Luttermann, Marcel Gehrke +1
To allow for principled comparison between two probabilistic graphical models defined over non-identical variable sets, they have to be lifted to a common measurable space. To this…
On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions
Malte Luttermann, Ralf Möller, Marcel Gehrke
Exploiting the indistinguishability of objects in a probabilistic graphical model such as a factor graph is key to lifted probabilistic inference algorithms and allows for tractabl…
Compression versus Accuracy: A Hierarchy of Lifted Models
Jan Speller, Malte Luttermann, Marcel Gehrke +1
Probabilistic graphical models that encode indistinguishable objects and relations among them use first-order logic constructs to compress a propositional factorised model for more…
Approximate Lifted Model Construction
Malte Luttermann, Jan Speller, Marcel Gehrke +3
Probabilistic relational models such as parametric factor graphs enable efficient (lifted) inference by exploiting the indistinguishability of objects. In lifted inference, a repre…
Lifting Factor Graphs with Some Unknown Factors for New Individuals
Malte Luttermann, Ralf Möller, Marcel Gehrke
Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while…