BayesiaLab
missing-values-processing-in-bayesialab
Infer Entropy Based Imputations

Infer — Entropy-Based Imputations

Under Infer, we have two additional options, namely Entropy-Based Static Imputation and Entropy-Based Dynamic Imputation. As their names imply, they are based on Static Imputation and Dynamic Imputation.

Whereas the standard (non-entropy-based) approaches randomly choose the sequence in which missing values are imputed within a row of data, the entropy-based methods select the order based on the conditional uncertainty associated with the unobserved variable. More specifically, missing values are imputed first for those variables that meet the following conditions:

  1. Variables that have a fully-observed/imputed Markov Blanket;
  2. Variables that have the lowest conditional entropy, given the observations and imputed values.

The advantages of the entropy-based methods are (a) the speed improvement over their corresponding standard methods and (b) their improved ability to handle datasets with large proportions of missing values.


For North America

Bayesia USA

4235 Hillsboro Pike
Suite 300-688
Nashville, TN 37215, USA

+1 888-386-8383
info@bayesia.us

Head Office

Bayesia S.A.S.

Parc Ceres, Batiment N 21
rue Ferdinand Buisson
53810 Change, France

For Asia/Pacific

Bayesia Singapore

1 Fusionopolis Place
#03-20 Galaxis
Singapore 138522


Copyright © 2024 Bayesia S.A.S., Bayesia USA, LLC, and Bayesia Singapore Pte. Ltd. All Rights Reserved.