Statistical analysis with missing data
Statistical analysis with missing data
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Measures of uncertainty in expert systems
Artificial Intelligence
Artificial Intelligence
Robust Learning with Missing Data
Machine Learning
A new approach to updating beliefs
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Updating beliefs with incomplete observations
Artificial Intelligence
Fast algorithms for robust classification with Bayesian nets
International Journal of Approximate Reasoning
Decision making under uncertainty using imprecise probabilities
International Journal of Approximate Reasoning
Marginal extension in the theory of coherent lower previsions
International Journal of Approximate Reasoning
A survey of the theory of coherent lower previsions
International Journal of Approximate Reasoning
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
The Journal of Machine Learning Research
International Journal of Approximate Reasoning
Artificial Intelligence
Updating coherent previsions on finite spaces
Fuzzy Sets and Systems
Journal of Artificial Intelligence Research
Ignorability in statistical and probabilistic inference
Journal of Artificial Intelligence Research
The inferential complexity of Bayesian and credal networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Graphical models for imprecise probabilities
International Journal of Approximate Reasoning
Learning from what you don't observe
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Independence concepts for convex sets of probabilities
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Modeling Unreliable Observations in Bayesian Networks by Credal Networks
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
International Journal of Approximate Reasoning
Epistemic irrelevance in credal nets: The case of imprecise Markov trees
International Journal of Approximate Reasoning
Likelihood-based Imprecise Regression
International Journal of Approximate Reasoning
Partially identified prevalence estimation under misclassification using the kappa coefficient
International Journal of Approximate Reasoning
Artificial Intelligence
Belief function and multivalued mapping robustness in statistical estimation
International Journal of Approximate Reasoning
Hi-index | 0.00 |
In this paper we formulate the problem of inference under incomplete information in very general terms. This includes modelling the process responsible for the incompleteness, which we call the incompleteness process. We allow the process' behaviour to be partly unknown. Then we use Walley's theory of coherent lower previsions, a generalisation of the Bayesian theory to imprecision, to derive the rule to update beliefs under incompleteness that logically follows from our assumptions, and that we call conservative inference rule. This rule has some remarkable properties: it is an abstract rule to update beliefs that can be applied in any situation or domain; it gives us the opportunity to be neither too optimistic nor too pessimistic about the incompleteness process, which is a necessary condition to draw reliable while strong enough conclusions; and it is a coherent rule, in the sense that it cannot lead to inconsistencies. We give examples to show how the new rule can be applied in expert systems, in parametric statistical inference, and in pattern classification, and discuss more generally the view of incompleteness processes defended here as well as some of its consequences.