TY - JOUR
T1 - The belief index
T2 - an empirical measure for evaluating outcomes in Bayesian belief network modelling
AU - Vilizzi, Lorenzo
AU - Price, Amina
AU - Beesley, Leah Simone
AU - Gawne, Ben
AU - King, Alison
AU - Koehn, John
AU - Meredith, Shaun
AU - Nielsen, Daryl
AU - Sharpe, Clayton
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
U2 - 10.1016/j.ecolmodel.2012.01.005
DO - 10.1016/j.ecolmodel.2012.01.005
M3 - Article
VL - 228
SP - 123
EP - 129
JO - Ecological Modelling
JF - Ecological Modelling
SN - 0304-3800
ER -