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3. Imprecise Classification with Non-parametric Predictive Inference

4. Learning Sets of Bayesian Networks

5. Estimating Conditional Probabilities by Mixtures of Low Order Conditional Distributions

7. Combination of Upper and Lower Probabilities

8. Calculating Uncertainty Intervals From Conditional Convex Sets of Probabilities

9. Partially Specified Belief Functions

10. Independence Concepts for Convex Sets of Probabilities

11. Inference with Idempotent Valuations

12. Credal C4.5 with Refinement of Parameters

13. Divergence Measures and Approximate Algorithms for Valuation Based Systems

14. Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models

20. Extended Probability Trees for Probabilistic Graphical Models

23. Learning Recursive Probability Trees from Data

25. Introduction

26. Locally Averaged Bayesian Dirichlet Metrics

28. Recursive Probability Trees for Bayesian Networks

29. An Importance Sampling Approach to Integrate Expert Knowledge When Learning Bayesian Networks From Data

30. A Bayesian Random Split to Build Ensembles of Classification Trees

31. Binary Probability Trees for Bayesian Networks Inference

36. Approximate Factorisation of Probability Trees

37. Abductive Inference in Bayesian Networks: Finding a Partition of the Explanation Space

38. Selective Gaussian Naïve Bayes Model for Diffuse Large-B-Cell Lymphoma Classification: Some Improvements in Preprocessing and Variable Elimination

40. Range of Entropy for Credal Sets

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