Lifted dynamic junction tree algorithm
Probabilistic models involving relational and temporal aspects need exact and efficient
inference algorithms. Existing approaches are approximative, include unnecessary …
inference algorithms. Existing approaches are approximative, include unnecessary …
[PDF][PDF] Parameterised Queries and Lifted Query Answering.
Parameterised Queries and Lifted Query Answering Page 1 Parameterised Queries and Lifted
Query Answering Tanya Braun, Ralf Möller Institute of Information Systems University of …
Query Answering Tanya Braun, Ralf Möller Institute of Information Systems University of …
[PDF][PDF] Taming exact inference in temporal probabilistic relational models
M Gehrke - 2019 - edit.fis.uni-hamburg.de
In information technology settings, multiple sensors gather data over time. With many
sensors constantly sending data, manually analysing data is infeasible. Therefore, such data …
sensors constantly sending data, manually analysing data is infeasible. Therefore, such data …
A Glimpse into Statistical Relational AI: The Power of Indistinguishability
T Braun - International Conference on Scalable Uncertainty …, 2022 - Springer
Statistical relational artificial intelligence, StaRAI for short, focuses on combining reasoning
in uncertain environments with reasoning about individuals and relations in those …
in uncertain environments with reasoning about individuals and relations in those …
Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries
efficiently for probabilistic relational temporal models by building and then reusing a first …
efficiently for probabilistic relational temporal models by building and then reusing a first …
Uncertain evidence for probabilistic relational models
Standard approaches for inference in probabilistic relational models include lifted variable
elimination (LVE) for single queries. To efficiently handle multiple queries, the lifted junction …
elimination (LVE) for single queries. To efficiently handle multiple queries, the lifted junction …
Adaptive inference on probabilistic relational models
Standard approaches for inference in probabilistic relational models include lifted variable
elimination (LVE) for single queries. To efficiently handle multiple queries, the lifted junction …
elimination (LVE) for single queries. To efficiently handle multiple queries, the lifted junction …
Towards preventing unnecessary groundings in the lifted dynamic junction tree algorithm
The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries
efficiently for probabilistic relational temporal models by building and then reusing a first …
efficiently for probabilistic relational temporal models by building and then reusing a first …
Answering multiple conjunctive queries with the lifted dynamic junction tree algorithm
The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries
efficiently for probabilistic relational temporal models by building and then reusing a first …
efficiently for probabilistic relational temporal models by building and then reusing a first …
Answering Hindsight Queries with Lifted Dynamic Junction Trees
The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction
queries for probabilistic relational temporal models by building and then reusing a first-order …
queries for probabilistic relational temporal models by building and then reusing a first-order …