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Rebernjak, Blaž1
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Uncertainty in Artificial Neural Network Models: Monte-Carlo Simulations beyond the GUM Boundaries
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A. M. Sadek; Fahad Al-Muhlaki – Measurement: Interdisciplinary Research and Perspectives, 2024
In this study, the accuracy of the artificial neural network (ANN) was assessed considering the uncertainties associated with the randomness of the data and the lack of learning. The Monte-Carlo algorithm was applied to simulate the randomness of the input variables and evaluate the output distribution. It has been shown that under certain…
Descriptors: Monte Carlo Methods, Accuracy, Artificial Intelligence, Guidelines
There Are Many Greater Lower Bounds than Cronbach's [alpha]: A Monte Carlo Simulation Study
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Novak, Josip; Rebernjak, Blaž – Measurement: Interdisciplinary Research and Perspectives, 2023
A Monte Carlo simulation study was conducted to examine the performance of [alpha], [lambda]2, [lambda][subscript 4], [lambda][subscript 2], [omega][subscript T], GLB[subscript MRFA], and GLB[subscript Algebraic] coefficients. Population reliability, distribution shape, sample size, test length, and number of response categories were varied…
Descriptors: Monte Carlo Methods, Evaluation Methods, Reliability, Simulation