Artificial Intelligence
Stochastic simulation
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
aHUGIN: a system creating adaptive causal probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Real-world applications of Bayesian networks
Communications of the ACM
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Bayesian networks for knowledge discovery
Advances in knowledge discovery and data mining
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Uncertainty in Artificial Intelligence
Uncertainty in Artificial Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Evidence Absorption and Propagation through Evidence Reversals
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Probabilistic Independence Networks for Hidden Markov Probability Models
Probabilistic Independence Networks for Hidden Markov Probability Models
Decision-theoretic foundations for causal reasoning
Journal of Artificial Intelligence Research
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A characterization of the dirichlet distribution with application to learning Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the testability of causal models with latent and instrumental variables
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning equivalence classes of Bayesian network structures
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
New Directions in Measurement for Software Quality Control
STEP '02 Proceedings of the 10th International Workshop on Software Technology and Engineering Practice
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
Building large-scale Bayesian networks
The Knowledge Engineering Review
Utilization of Hierarchical, Stochastic Relationship Modeling for Hangul Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Non-Stationary Dynamic Bayesian Networks
The Journal of Machine Learning Research
Automatic construction of bayesian network structures by means of a concurrent search mechanism
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Bayesian automatic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector
Environmental Modelling & Software
Enhancing the Adaptation of BDI Agents Using Learning Techniques
International Journal of Agent Technologies and Systems
| Hi-index | 0.00 |
A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influences between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the parameters of a model. We then progress to showing how the structure of the model can be revised as data is obtained. Techniques for learning with incomplete data are also covered. In order to make the paper as self contained as possible, we start with an introduction to probability theory and probabilistic graphical models. The paper concludes with a short discussion on how these techniques can be applied to the problem of learning causal relationships between variables in a domain of interest.