R. Mead, J. Paxton, and R. Sojda (USA)
Bayesian Belief Networks, Modelling and Simulation of Ecosystems, Statistics
Bayes’ rule not only opens the door to systems that evolve probabilities as new evidence is acquired, but also, as will be seen in the next section, provides the underpinning for the inferential mechanisms used in Bayesian belief networks [1]. Bayesian belief networks are a popular tool for reasoning under uncertainty. Certain advantages make them well suited for applications in ecological modelling. In this paper, we provide an overview of Bayesian belief networks and offer examples of their use in ecological modelling. We also review hierarchical Bayesian modelling and influence diagrams. Despite its benefits, the Bayesian approach also has drawbacks. One drawback is the difficulty of obtaining accurate conditional probabilities. When adequate data is unavailable, sometimes experts must estimate the missing probabilities subjectively [2]. Another drawback is that the approach can be computationally intensive, especially when the variables being studied are not conditionally independent of one another.
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