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Comparing the original and modified models on synthetic and real data, the contributions of our modification are shown.
We evaluate the models on synthetic and real data sets.
We evaluate the proposed models on synthetic and real data sets in section "Evaluate the proposed models".
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We address the question using computer simulation modeling on synthetic landscapes representing both fire regime treatments.
There is a factor of 3-10x in accuracy between a well-trained model on synthetic data versus real-world data.
This is far lower than the 90%% reported by Ott et al. when evaluating their model on synthetic data.
We evaluated the performance of clustering model on synthetic data set, for which the ground truth is available, and then we evaluated the performance of RLEM model on three data sets.
To warrant reliable conclusions, we calibrated the model on synthetic data and the golden-spike experiment from Choe et al. (2005), before analysing 14 microarray datasets.
Next, we compared the power of our LRT approach to a score test approach (both using the same two random effects model) on synthetic data (see Section 2).
At last, we test the models on both synthetic and real images.
We evaluate and compare our models on both synthetic and real data sets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com