Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference in multiply connected belief networks using loop cutsets
International Journal of Approximate Reasoning
An analysis of first-order logics of probability
Artificial Intelligence
The complexity of finite functions
Handbook of theoretical computer science (vol. A)
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Automatic symbolic traffic scene analysis using belief networks
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Algorithmic theory of random graphs
Random Structures & Algorithms - Special issue: average-case analysis of algorithms
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
On the complexity of inference about probabilistic relational models
Artificial Intelligence
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Pruning bayesian networks for efficient computation
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Convergence Results for Relational Bayesian Networks
LICS '98 Proceedings of the 13th Annual IEEE Symposium on Logic in Computer Science
Representing and reasoning with probabilistic knowledge
Representing and reasoning with probabilistic knowledge
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Parameter learning for relational Bayesian networks
Proceedings of the 24th international conference on Machine learning
MEBN: A language for first-order Bayesian knowledge bases
Artificial Intelligence
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Probabilistic logic with independence
International Journal of Approximate Reasoning
A glimpse of symbolic-statistical modeling by PRISM
Journal of Intelligent Information Systems
Probabilistic-Logic Models: Reasoning and Learning with Relational Structures
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Compiling relational Bayesian networks for exact inference
International Journal of Approximate Reasoning
New advances in logic-based probabilistic modeling by PRISM
Probabilistic inductive logic programming
Model-theoretic expressivity analysis
Probabilistic inductive logic programming
Negation elimination for finite PCFGs
LOPSTR'04 Proceedings of the 14th international conference on Logic Based Program Synthesis and Transformation
Probabilistic dialogue models with prior domain knowledge
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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A number of representation systems have been proposed that extend the purely propositional Bayesian network paradigm with representation tools for some types of first-order probabilistic dependencies. Examples of such systems are dynamic Bayesian networks and systems for knowledge based model construction. We can identify the representation of probabilistic relational models as a common well-defined semantic core of such systems.Recursive relational Bayesian networks (RRBNs) are a framework for the representation of probabilistic relational models. A main design goal for RRBNs is to achieve greatest possible expressiveness with as few elementary syntactic constructs as possible. The advantage of such an approach is that a system based on a small number of elementary constructs will be much more amenable to a thorough mathematical investigation of its semantic and algorithmic properties than a system based on a larger number of high-level constructs. In this paper we show that with RRBNs we have achieved our goal, by showing, first, how to solve within that framework a number of non-trivial representation problems. In the second part of the paper we show how to construct from a RRBN and a specific query, a standard Bayesian network in which the answer to the query can be computed with standard inference algorithms. Here the simplicity of the underlying representation framework greatly facilitates the development of simple algorithms and correctness proofs. As a result we obtain a construction algorithm that even for RRBNs that represent models for complex first-order and statistical dependencies generates standard Bayesian networks of size polynomial in the size of the domain given in a specific application instance.