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A wide family of models is obtained, for which inference, testing hypotheses, model choice and model validation problems are dealt with.
Therefore, autocorrelation can have several implications for calculating statistics of measurement data in terms of statistical inference testing [17, 18].
In our work we provide a global inference, testing the significativity of the spatial pattern obtained by ODT.
Unlike statistical inference testing methods based on analytical null-hypothesis distributions, network centrality scores and Relief-F scores lack an analytical null distribution.
Then, for inference testing, we only included a neuron when the block transition in the analysed direction consisted of a switch between that neurons preferred to anti-preferred reward feature, or between its anti-preferred to preferred reward feature.
Therefore, auto-correlation can have several implications for, e.g. statistical inference testing and regression analysis [64].
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An inference tests at the grid point level was applied to assess if the ratio is statistically significantly above or below 1 with 90% confidence using a two-sided F-test that takes into account the autocorrelation of the observation minus prediction time series, but no point was found significant.
In other words, MC inference tested the ODT-model and provided the significance of the spatial pattern issued from the oblique decision tree.
Situational awareness (SA) develops when individuals, involved within a certain situation, look around, gather information about the situation, make inferences, test their inferences, and draw further inferences from the results [10].
The further development of molecular evolutionary inference tests to infer natural selection acting upon chromatin, in addition to comparative studies conducted both within and between populations will eventually help in attaining this goal.
Disentangling molecular relationships with a causal inference test.
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