Dynamic Imputation
Context
- Dynamic Imputation is the first of a range of Missing Values Processing algorithms that takes advantage of the structural learning algorithms available in BayesiaLab.
- Like Static Imputation, Dynamic Imputation is probabilistic; imputed values are drawn from distributions.
- However, unlike Static Imputation, Dynamic Imputation does not only perform imputation once, but rather whenever the current model is modified, i.e., after each arc addition, deletion, and reversal during structural learning.
- This way, Dynamic Imputation always uses the latest network structure for updating the distributions from which the imputed values are drawn.
Usage
- Select
Menu > Learning > Missing Values Processing > Dynamic Imputation.
For a thorough introduction to this topic, please see Chapter 9: Missing Values Processing in our e-book.