• Journal of the Korean Mathematical Society
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• v.32 no.1
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• pp.85-92
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• 1995

Supppose ${X_n}$ is a Markov process taking values in some arbitrary state space $(S, F)$ with temporarily homogeneous transition probabilities $p^n(x, A) = P(X_n \in $A\mid$X_0 = x), x \in S, A \in F$. Write $p(x, A) for p^1(x, A)$.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.4
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• pp.89-97
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• 2003

Using inference models developed for estimation of the parameters necessary to implement the Runoff Block of the Stormwater Management Model (SWMM), a number of alternative inference scenarios were developed to assess the influence of inference model complexity and structure on the calibration of the catchment modelling system. These inference models varied from the assumption of a spatially invariant value (catchment average) to spatially variable with each subcatchment having its own unique values. Fur-thermore, the influence of different measures of deviation between the recorded information and simulation predictions were considered. The results of these investigations indicate that the model performance is more influenced by model structure than complexity, and control parameter values are very much dependent on objective function selected as this factor was the most influential for both the initial estimates and the final results.