.png){kind=icon}
Return
Robust learning with missing data
.png){kind=icon}
OA
AI
DOI：10.1023/A:1010968702992.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper introduces a new method, called the robust Bayesian estimator (RBE), to learn conditional probability distributions from incomplete data sets. The intuition behind the RBE is that, when no information about the pattern of missing data is available, an incomplete database constrains the set of all possible estimates and this paper provides a characterization of these constraints. An experimental comparison with two popular methods to estimate conditional probability distributions from incomplete data-Gibbs sampling and the EM algorithm-shows a gain in robustness. An application of the RBE to quantify a naive Bayesian classifier from an incomplete data set illustrates its practical relevance.
Keywords:
Bayesian learning
Bayesian networks
Bayesian classifiers
probability intervals
missing data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.9
Papers:
2.7K
Citations:
3.4W
Organization
No organization information available
Cited Papers
No cited papers available