"Bayesian Networks: Advances and Novel Applications" ed. by Douglas McNair

Contributors to this volume elucidate various new developments in these aspects of Bayesian networks (BNs) .

Bayesian networks (BN) have recently experienced increased interest and diverse applications in numerous areas, including economics, risk analysis and assets and liabilities management, AI and robotics, transportation systems planning and optimization, political science analytics, law and forensic science assessment of agency and culpability, pharmacology and pharmacogenomics, systems biology and metabolomics, psychology, and policy-making and social programs evaluation. This strong and varied response results not least from the fact that plausibilistic Bayesian models of structures and processes can be robust and stable representations of causal relationships. Additionally, BNs' amenability to incremental or longitudinal improvement through incorporating new data affords extra advantages compared to traditional frequentist statistical methods.

Contents
1.Introductory Chapter: Timeliness of Advantages of Bayesian Networks
2.An Economic Growth Model Using Hierarchical Bayesian Method
3.Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation
4.Using Bayesian Networks for Risk Assessment in Healthcare System
5.Continuous Learning of the Structure of Bayesian Networks: A Mapping Study
6.Multimodal Bayesian Network for Artificial Perception
7.Quantitative Structure-Activity Relationship Modeling and Bayesian Networks: Optimality of Naive Bayes Model
8.Bayesian Graphical Model Application for Monetary Policy and Macroeconomic Performance in Nigeria