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
A tutorial on learning with Bayesian networks
Learning in graphical models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
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
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Loglinear models for first-order probabilistic reasoning
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Data-driven evolution of data mining algorithms
Communications of the ACM - Evolving data mining into solutions for insights
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
Naive Bayesian Classification of Structured Data
Machine Learning
Case-factor diagrams for structured probabilistic modeling
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Interactive global illumination in dynamic participating media using selective photon tracing
Proceedings of the 21st spring conference on Computer graphics
Leveraging relational autocorrelation with latent group models
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Quantitative pharmacophore models with inductive logic programming
Machine Learning
Diagnosis using a first-order stochastic language that learns
Expert Systems with Applications: An International Journal
Learning and inferring transportation routines
Artificial Intelligence
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Probabilistic relational models (PRMs) are a language for describing statistical models over typed relational domains. A PRM models the uncertainty over the attributes of objects in the domain and uncertainty over the relations between the objects. The model specifies, for each attribute of an object, its (probabilistic) dependence on other attributes of that object and on attributes of related objects. The dependence model is defined at the level of classes of objects. The class dependence model is instantiated for any object in the class, as appropriate to the particular context of the object (i.e., the relations between this objects and others). PRMs can also represent uncertainty over the relational structure itself, e.g., by specifying a (class-level) probability that two objects will be related to each other. PRMs provide a foundation for dealing with the noise and uncertainty encountered in most real-world domains. In this chapter, we show that the compact and natural representation of PRMs allows them to be learned directly form an existing relational database using well-founded statistical techniques. We give an introduction to PRMs and an overview of methods for learning them. We show that PRMs provide a new framework for relational data mining, and offer new challenges for the endeavor of learning relational models for real-world domains.