A method for evaluating elicitation schemes for probabilistic models

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 32, Issue 1, 2002, Pages 38-43

  Wang, Haiqin ^a   Dash, Denver ^a   Druzdzel, Marek J ^a  

^a UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Bayesian network; Evaluation of elicitation methods learning; Probability elicitation

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; LEARNING ALGORITHMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY DISTRIBUTIONS; VIRTUAL REALITY;

BAYESIAN NETWORK; PROBABILITY ELICITATION; PROBABILITY WHEEL; SCALED PROBABILITY BAR;

PROBABILITY;

EID: 0036465376     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/3477.979958     Document Type: Letter

References (20)

1. Building probabilistic networks: 'Where do the numbers come from'? Guest editors' introduction
2. The cognitive side of probability learning
3. Probability encoding in decision analysis
4. User interface tools for navigation in conditional probability tables and graphical elicitation of probabilities in Bayesian networks
5. Judged frequency of lethal events
6. Effects of a relative-frequency elicitation question on likelihood judgment accuracy: The case of external correspondence
7. Assessment of probability distributions for continuous random variables: A comparison of the bisection and fixed value methods
8. Calibration of probabilities: The state of the art to 1980
9. How to elicit many probabilities
10. A theory of inferred causation
11. A Bayesian method for the induction of probabilistic networks from data
12. Bayesian networks for data mining
13. A characterization of the Dirichlet distribution with application to learning Bayesian networks
14. Propagating uncertainty in Bayesian networks by probabilistic logic sampling
15. GeNIe: A development environment for graphical decision-theoretic models
16. Extension of the Hepar II model to multiple-disorder diagnosis
17. A theory of inferred causation