A Graphical Approach to Diagnosing the Validity of the Conditional Independence Assumptions of a Bayesian Network Given Data
| Autor: | Stephen Walsh, Paul D. Whitney |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Statistics and Probability
Structure (mathematical logic) Computer science Bayesian network Inference Expert elicitation computer.software_genre Consistency (database systems) Conditional independence Discrete Mathematics and Combinatorics Independence (mathematical logic) Domain knowledge Data mining Statistics Probability and Uncertainty computer |
| Zdroj: | Journal of Computational and Graphical Statistics. 21:961-978 |
| ISSN: | 1537-2715 1061-8600 |
| Popis: | Bayesian networks (BNs) have attained widespread use in data analysis and decision making. Well-studied topics include efficient inference, evidence propagation, parameter learning from data for complete and incomplete data scenarios, expert elicitation for calibrating BN probabilities, and structure learning. It is common for the researcher to assume the structure of the BN or to glean the structure from expert elicitation or domain knowledge. In this scenario, the model may be calibrated through learning the parameters from relevant data. There is a lack of work on model diagnostics for fitted BNs; this is the contribution of this article. We key on the definition of (conditional) independence to develop a graphical diagnostic that indicates whether the conditional independence assumptions imposed, when one assumes the structure of the BN, are supported by the data. We develop the approach theoretically and describe a Monte Carlo method to generate uncertainty measures for the consistency of the data wi... |
| Databáze: | OpenAIRE |
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