Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning
Belief Update in Bayesian Networks Using Uncertain Evidence
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Extending Markov Logic to Model Probability Distributions in Relational Domains
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Towards efficient sampling: exploiting random walk strategies
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
On the use of virtual evidence in conditional random fields
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
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The key task in probabilistic reasoning is to appropriately update one's beliefs as one obtains new information in the form of evidence. In many application settings, however, the evidence we obtain as input to an inference problem may be uncertain (e.g. owing to unreliable mechanisms with which we obtain the evidence) or may correspond to (soft) degrees of belief rather than hard logical facts. So far, methods for updating beliefs in the light of soft evidence have been centred around the iterative proportional fitting procedure and variations thereof. In this work, we propose a Markov chain Monte Carlo method that allows to directly integrate soft evidence into the inference procedure without generating substantial computational overhead. Within the framework of Markov logic networks, we demonstrate the potential benefit of this method over standard approaches in a series of experiments on synthetic and real-world applications.