Learning Bayesian network structure using Markov blanket decomposition

TL;DR: A novel hybrid structure learning algorithm based on the idea of model decomposition, which takes into account the knowledge of local neighborhood structures is proposed, which generally gains the better performance of structure recovery than other representative methods, especially for large-scale BNs.

TL;DR: This study used a Bayesian network approach to identify the injury type, victim category and time to evaluation as the main determinants of the ‘Total Incapacity to Work’ (TIW) and computed the conditional probabilities associated with the TIW node and its parents.

TL;DR: This research uncovers the best core set of relevant indices for predicting other bibliometric indices and uses Gaussian Bayesian networks learnt by a genetic algorithm that looks for the optimal models that best predict bibliometry data.

TL;DR: This work proposes the framework of incremental structure learning and a new evaluation criterion “ABIC” (Adopt Bayesian Information Criterion) based on the BIC and shows that the proposed algorithm can greatly improve the accuracy of the structure and the total of learning time is greatly reduced.

TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.

TL;DR: The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.

TL;DR: In this paper, variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available, such as t...

Abstract: 1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpson's paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect.