Bayesian networks (BN) are probabilistic graphical models that can capture both quantitative and qualitative data to structure complex management problems (Ames et al., 2005). The steps for the BN implementation include:
Some of the major advantages of Bayesian networks are:
- Integrating different types of information such as expert judgement, literature review and empirical data
- Allowing the model to be progressively improved, by easily updating all its components considering new advances in the state-of-the-art of the topic or new data
- Discussion and interaction are facilitated using a graphical output, called a conceptual model.
