Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Readings in computer vision: issues, problems, principles, and paradigms
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Chordal completions of planar graphs
Journal of Combinatorial Theory Series B
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Bayesian Network Learning with Parameter Constraints
The Journal of Machine Learning Research
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Constrained Maximum Likelihood Learning of Bayesian Networks for Facial Action Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Learning from measurements in exponential families
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Joint unsupervised coreference resolution with Markov logic
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Relational probabilistic conditional reasoning at maximum entropy
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Graphical models and exponential families
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Markov logic is a rich language that allows one to specify a knowledge base as a set of weighted first-order logic formulas, and to define a probability distribution over truth assignments to ground atoms using this knowledge base. Usually, the weight of a formula cannot be related to the probability of the formula without taking into account the weights of the other formulas. In general, this is not an issue, since the weights are learned from training data. However, in many domains (e.g. healthcare, dependable systems, etc.), only little or no training data may be available, but one has access to a domain expert whose knowledge is available in the form of subjective probabilities. Within the framework of Bayesian statistics, we present a formalism for using a domain expert's knowledge for weight learning. Our approach defines priors that are different from and more general than previously used Gaussian priors over weights. We show how one can learn weights in an MLN by combining subjective probabilities and training data, without requiring that the domain expert provides consistent knowledge. Additionally, we also provide a formalism for capturing conditional subjective probabilities, which are often easier to obtain and more reliable than non-conditional probabilities. We demonstrate the effectiveness of our approach by extensive experiments in a domain that models failure dependencies in a cyber-physical system. Moreover, we demonstrate the advantages of using our proposed prior over that of using non-zero mean Gaussian priors in a commonly cited social network MLN testbed.