Behailu Getachew

Application of Bayesian Belief Network on Biological Traits: Uncertainty Management Technique to Support Biological Modeling for Conservation of Threatened Tree Species Behailu Getachew

Developing models for inference have been a problem in the domain area in particular for threatened tree species which have inevitable contribution for securing the natural resource conservation and its genetic stability from being eroded as indicated in many literatures. Over the past half century, Bayesian methods have emerged as a preferred method for reasoning with uncertainty due to their mathematical foundation. Although Bayesian theory does not solve all problems in probabilistic reasoning, it has given scientists a sound framework within which uncertainty can be represented and analyzed pragmatically. By looking at systems probabilistically, the models constructed explicitly represent the uncertainty in the underlying system. Bayesian belief networks (BBNs) are useful tools for modeling biological predictions and aiding species conservation and managing uncertainty in decision-making. The work shows that the Bayesian network classifier has a potential to be used as a tool for prediction of biological modeling to forward about conservation actions in the field of forestry.