The belief index: an empirical measure for evaluating outcomes in Bayesian belief network modelling

Lorenzo Vilizzi, Amina Price, Leah Beesley, Ben Gawne, Alison Jane King, John Koehn, Shaun Meredith, Daryl Nielsen, Clayton Sharpe

Research output: Contribution to journalArticle

Abstract

Bayesian belief networks (BBNs) are a widespread tool for modelling the effects of management decisions and activities on a variety of environmental and ecological responses. Parameterisation of BBNs is often achieved by elicitation involving multiple experts, and this may result in different conditional probability distribution tables for the nodes in a BBN. Another common use of BBNs is in the comparison of alternative management scenarios. This paper describes and implements the ‘belief index’ (BI), an empirical measure for evaluating outcomes in BBN modelling that summarises the probabilities (or beliefs) of any one node in a BBN. A set of four species-specific BBNs for managing watering events for wetland fish is outlined and used to statistically assess between-expert and between-species variability in parameter estimates by means of the BI. Different scenarios for management decisions are also compared using the % improvement measure, a derivative of the BI.
Original languageEnglish
Pages (from-to)123-129
Number of pages7
JournalEcological Modelling
Volume228
DOIs
Publication statusPublished - 10 Mar 2012
Externally publishedYes

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