Contents

#### **Preface XI**

Chapter 1 **Introductory Chapter: Timeliness of Advantages of Bayesian Networks 1** Douglas S. McNair

Chapter 2 **An Economic Growth Model Using Hierarchical Bayesian Method 5** Nur Iriawan and Septia Devi Prihastuti Yasmirullah


Chapter 8 **Bayesian Graphical Model Application for Monetary Policy and Macroeconomic Performance in Nigeria 111** David Oluseun Olayungbo

Preface

graduate or postgraduate level.

In recent years, Bayesian networks have experienced increased interest and widely varied 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, pharmacol‐ ogy and pharmacogenomics, systems biology and metabolomics, psychology, and policymaking and social programs evaluation. This strong and diverse response results not least from the fact that plausibilistic Bayesian models of structures and processes can be robust and stable representations of causal relationships. Such stability and resilience to multisourced data has allowed to design practical solutions that yield important and novel in‐ sights. Additionally, Bayesian networks' amenability to incremental or longitudinal improvement through incorporating new data affords extra advantages compared to tradi‐ tional frequentist statistical methods. We have created this volume with a view toward col‐ leagues in the field of machine learning and Bayesian networks and to students at the

In terms of epistemology, Bayesian networks promise to help achieve improved accuracy regarding the truth of propositions of interest and regarding the causal and statistical basis for their truth. Moreover, Bayesian networks can reveal relationships that have face-validity to decision-makers and the public. To the degree that they illuminate a credible basis for particular probabilistic solutions, such improvements can enable setting forth mechanisms and principles in a defensible way, supported by a basis that can anchor just and stable poli‐ cy. The present volume includes new contributions from a number of innovators in Bayesi‐

In the Introduction, I call attention to the contemporary relevance of Bayesian networks, in a time and culture that needs epistemological 'ground truth', or as near as one can come to this, as a basis for rational, ethical management of social, engineering, and biological sys‐ tems. Bayesian networks are particularly effective in this connection insofar as the arcs that are empirically learned or induced for the networks very often accurately represent the di‐ rection of causality. Events or states that share a de facto cause are likely to be conditionally independent given the cause; arrows in the causal direction capture this independence. In a naïve Bayes network, the arcs are often not in the right causal direction (e.g., diabetes does not cause aging). But in non-naïve and other types, the arcs are highly accurate regarding causality. This aspect is not only valuable as to low error rates in practical applications but also affords a greater degree of face-validity, transparency, and social and psychological ac‐ ceptability compared to certain other machine-learning methods and AI model types.

Chapter 2 by Septia Yasmirullah and Nur Iriawan concerns growth model approaches using hierarchical Bayesian methods. They address emerging economic imbalances within Indone‐ sian regions, subsequent to 2004. Economic development naturally entails issues of fairness and equitability of resources application as well as determinations of programs' efficacy. Ac‐

an networks with emphasis on socially important applications.
