Showing 2024Show all
3 papers · 1 filter
cs.AI2024
Lifted Model Construction without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs
Malte Luttermann, Ralf Möller, Marcel Gehrke
Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes of logical variables. We fou…
cs.AI2024
Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs
Malte Luttermann, Tanya Braun, Ralf Möller +1
Lifting uses a representative of indistinguishable individuals to exploit symmetries in probabilistic relational models, denoted as parametric factor graphs, to speed up inference…
cs.AI2024
Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models
Malte Luttermann, Ralf Möller, Mattis Hartwig
Probabilistic relational models provide a well-established formalism to combine first-order logic and probabilistic models, thereby allowing to represent relationships between obje…