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MCL: Model Checking Language; Alternation-Free Modal μ-calculus extended with user-friendly regular expressions and value-passing constructs; subsumes CTL and LTL. mCRL2 mu-calculus: Kozen's propositional modal μ-calculus (excluding atomic propositions), extended with: data-depended processes, quantification over data types, multi-actions ...
A test significance for NSE to assess its robustness has been proposed whereby the model can be objectively accepted or rejected based on the probability value of obtaining NSE greater than some subjective threshold. Nash–Sutcliffe efficiency can be used to quantitatively describe the accuracy of model outputs other than discharge.
The threshold value to determine when a data point fits a model (t), and the number of inliers (data points fitted to the model within t) required to assert that the model fits well to data (d) are determined based on specific requirements of the application and the dataset, and possibly based on experimental evaluation.
The output has most of its weight where the "4" was in the original input. This is what the function is normally used for: to highlight the largest values and suppress values which are significantly below the maximum value. But note: a change of temperature changes the output.
If p is a probability, then p/(1 − p) is the corresponding odds; the logit of the probability is the logarithm of the odds, i.e.: = = = = (). The base of the logarithm function used is of little importance in the present article, as long as it is greater than 1, but the natural logarithm with base e is the one most often used.
Linear regression can be used to estimate the values of β 1 and β 2 from the measured data. This model is non-linear in the time variable, but it is linear in the parameters β 1 and β 2; if we take regressors x i = (x i1, x i2) = (t i, t i 2), the model takes on the standard form
PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.
In addition to model-checking, SPIN can also operate as a simulator, following one possible execution path through the system and presenting the resulting execution trace to the user. Unlike many model-checkers, SPIN does not actually perform model-checking itself, but instead generates C sources for a problem-specific model checker.