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 perform imputation only 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 Menus > Learning > Missing Values Processing > Dynamic Imputation.