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Stochastic dynamics thus stands in sharp contrast to deterministic dynamics.
The results demonstrate that our stochastic approach provides different model decisions compared to the traditional deterministic approach.
Whereas the stochastic models work in situations where chance dominates, for example when the number of cells is small, or under random mutations, the deterministic models are more important for large-scale, normal hematopoiesis.
Results show that stochastic modelling considerably increases the uncertainty of parameter estimates, but ensures their consistency between separate trainings, whereas deterministic models are less robust and offer a less reliable forecasting.
Therefore, in some circumstances deterministic modelling may offer water resource managers a pragmatic alternative to stochastic modelling, but its usefulness as a surrogate will depend upon the level of uncertainty in the model parameters.
It is shown that when the system of interest is stochastic the expected variability of a stochastic parameter is biased when a deterministic model is employed for parameter estimation.
Our results provide support for the contention that stochastic modeling in economics should be more than a mere extension of the deterministic realm.
The stochastic model implemented into the deterministic model results in almost similar predictions with the deterministic model in 50% (best guess) probability.
We show that the dynamics of the system are influenced by both stochastic and deterministic processes.
We show that the stochastic model predicts the deterministic behavior on a reasonable time scale, which can be consistently obtained from both models.
Prediction accuracies, as well as the results of a test for the existence of determinism point to the conclusion that many of the seemingly stochastic series considered were deterministic.
These studies demonstrate that results from the deterministic analysis method are realizable in the stochastic analysis method.
These significant differences between the stochastic and the deterministic model results can serve to confirm the shortfalls of deterministic design.
This analysis has shown close correspondence between the stochastic and deterministic modelling results.
Open accessJournal ArticleDOI
Sandor Csorgo, Jan Mielniczuk 
01 Apr 1999-Bernoulli
56 Citations
The main results contrast with previous findings for deterministic-design regression.

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