with a single value reported for k(_oc) and t(^soil_1/2) was used as prior information for the EA data. Bayesian methods to analyse the EA data are proposed in this thesis. These methods use logistic regression with random covariates and prior information derives from (i) available United States Department of Agriculture (USDA) data base values of k(_oc) and t(^soil_1/2) for the covariates and (ii) Gustafson's data for the regression parameters.
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