Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
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
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Solving Selection Problems Using Preference Relation Based on Bayesian Learning
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Adaptive Bayesian Logic Programs
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
A Probabilistic Relational Student Model for Virtual Laboratories
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
ReMauve: A Relational Model Tree Learner
Inductive Logic Programming
A Probabilistic Relational Student Model for Virtual Laboratories
UM '07 Proceedings of the 11th international conference on User Modeling
Refining aggregate conditions in relational learning
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Combining bayesian networks with higher-order data representations
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Probabilistic first-order theory revision from examples
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
Probabilistic models provide a sound and coherent foundation for dealing with the noise and uncertainty encountered in most real-world domains. Bayesian networks are a language for representing complex probabilistic models in a compact and natural way. A Bayesian network can be used to reason about any attribute in the domain, given any set of observations. It can thus be used for a variety tasks, including prediction, explanation, and decision making. The probabilistic semantics also gives a strong foundation for the task of learning models from data. Techniques currently exist for learning both the structure and the parameters, for dealing with missing data and hidden variables, and for discovering causal structure. One of the main limitations of Bayesian networks is that they represent the world in terms of a fixed set of "attributes". Like propositional logic, they are incapable of reasoning explicitly about entities, and thus cannot represent models over domains where the set of entities and the relations between them are not fixed in advance. As a consequence, Bayesian networks are limited in their ability to model large and complex domains. Probabilistic relational models are a language for describing probabilistic models based on the significantly more expressive basis of relational logic. They allow the domain to be represented in terms of entities, their properties, and the relations between them. These models represent the uncertainty over the properties of an entity, representing its probabilistic dependence both on other properties of that entity and on properties of related entities. They can even represent uncertainty over the relational structure itself. Some of the techniques for Bayesian network learning can be generalized to this setting, but the learning problem is far from solved. Probabilistic relational models provide a new framework, and new challenges, for the endeavor of learning relational models for real-world domains.