On the Use of Bayesian Probability Networks with Hypothesized Malaria Influence Diagrams
Emmanuel Segun Oguntade, Awogbemi Adeyeye, Ilori Kolawole · International Journal of Statistical Distributions and Applications · 2025
Bayesian Belief Network (BBN) is an emerging modeling technique that provides a decision support framework for problem relating to uncertainty, complexity and probabilistic cognitive. The specification of Bayesian network is made up of graph structure of networks (qualitative part) and the specification of the conditional probability distributions (quantitative part). The technique conceptualizes a system of interest as a network of connected nodes and linkages. In spite of the versatile and general acceptability of estimation of disease cases from various available methods in literature, incorporating model uncertainty remains an open issue. In this article, we derived a probability based graphical model using expert opinions in related studies on malaria and its hypothesized predictors with a BBN. This approach is well applied in ecological studies and other environmental sciences in recent times for various estimations and predictions based on Bayesian reasoning. The study therefore examines the application of BBN with a view to estimate the model parameters by deriving a probability based networks applicable to malaria epidemics. While Markov Chain principles were explored as they relates to a BBN formulation and useful guidelines for developing the preliminary structure of the network, the topology of a BBN was derived as a directed acyclic graph with malaria predictors as network nodes.