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Found 8 papers, 7 papers with code
Efficient Detection of Commutative Factors in Factor Graphs
Lifted probabilistic inference exploits symmetries in probabilistic graphical models to allow for tractable probabilistic inference with respect to domain sizes.
Lifting Factor Graphs with Some Unknown Factors
Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while maintaining exact answers.
Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain
Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process.
Lifted Causal Inference in Relational Domains
Lifted inference exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.
Efficient Detection of Exchangeable Factors in Factor Graphs
In particular, we introduce the detection of exchangeable factors (DEFT) algorithm, which allows us to drastically reduce the computational effort for checking whether two factors are exchangeable in practice.
Colour Passing Revisited: Lifted Model Construction with Commutative Factors
Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes.
Practical Algorithms for Orientations of Partially Directed Graphical Models
In observational studies, the true causal model is typically unknown and needs to be estimated from available observational and limited experimental data.
Efficient Enumeration of Markov Equivalent DAGs
Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class (MEC) is an important primitive in causal analysis.
