A general framework for maximizing likelihood under incomplete data.
Inés CousoDidier DuboisPublished in: Int. J. Approx. Reason. (2018)
Keyphrases
- incomplete data
- learning bayesian networks
- missing data
- missing values
- em algorithm
- bayesian networks
- maximum likelihood
- incomplete data sets
- conditional likelihood
- posterior probability
- variational bayesian
- typical testors
- training data
- databases
- bayes classifier
- proximal point
- database
- input data
- support vector
- irrelevant attributes
- multiple imputation
- data sets