In this study, we propose that the learning performance of RBM function approximation can be further improved by computing the output by the negative expected energy (EE-RBM), instead of the negative free energy.
Background noise of each experiment was evaluated by computing the standard deviation of negative control intensities.
To eliminate the effect of the preceding P1, the N1 amplitude was measured by computing the difference between the most negative peak within 200 300 ms and the preceding most positive peak within 120 200 ms after the prize stimulus.
We then summarized all these correlations by computing the fraction of them more negative than a correlation threshold t, multiple values of which (-0.1 to -0.7 with a step size of 0.1) were tested.
They were used to evaluate our TFBS and the inferred TF-target predictions by computing the True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN) rates for some human transcription factors.
Further, we assessed the validation by computing the actual number of false positives and true negatives.
As done with the simulations, the diameter of the sutures was obtained by computing the time between the first positive peak and the second negative peak.
For a non-negative stochastic process, almost sure convergence to 0 can be tested by computing the (approximation over finitely many paths, of the) mean of sample paths.
This problem can be solved by computing the largest singular value and associated singular vectors of a permuted version of A. If A is symmetric, definite, non-negative, or banded, then the minimizing B and C are similarly structured.
You measure the opportunity cost by computing the value of the road not taken.
