For a dataset generated from this null model, the p-values should be uniformly distributed from 0 to 1, exclusive: the highest Kendall’s 1‘ out of N tests should have a p-value of 1 / (N + 1), the second highest test statistic has a p-value of 2/ (N + 1), and the ith highest test statistic has a p-value of i/ (N + 1) [35].

In the present study, these “empirical” p-values are based on 2 x 106 random time series and are nearly uniformly distributed , as desired (Fig.

The second simulated dataset contains 10% rhythmic time series of triangle waveform with uniformly distributed phases and asymmetries and 90% time series consisting solely of Gaussian noise.

3A we generated 10,000 time series with uniformly distributed random phase shifts (always with a 24 h period) and added Gaussian noise to each point with a standard deviation of 25% or 50% of the total waveform amplitude, examples of which can be seen in Fig.

In both cases, phases (peak values) were uniformly distributed over the possible discrete values.

Like in the simulation study, p0 was initialized by drawing randomly from a uniform distribution .

algorithm, p0 is initialized With a random draW from the uniform distribution and for 9 we use a uniform initial configuration.

The NEMix parameter p0 was initialized by drawing from a uniform distribution in each EM restart.

In the absence of further knowledge, the prior is usually set to the uniform distribution .

The estimation was performed for 100 uniformly distributed initial values (in the range of the parameter boundaries) for the parameters which enabled us to derive the parameter correlations (S4 Fig).

In Model-1, mCLB is uniformly distributed in the cell.

Parameter correlations derived from 100 fits started With uniformly distributed parameters Within the parameter boundaries (axis ranges) for Model-1 (red) and Model-2 (blue).

Distribution of parameter and objective values derived from 100 fits started With uniformly distributed parameters Within the parameter boundaries for

In our default implementation, we assumed a uniform distribution for possible dosages within any given ploidy.

We employed the same uniform distribution to simulate allele dosages.

We then assume a uniform distribution for all possible heterozygous proportions, as done by others in a notes the dosage of the first allele, with g = 1, - - -, M—l.

both possibilities are uniformly distributed .

Specificically the circular correlation coefficient can only be used for uniform distributions (i.e.

However, the more general vector formulation of the circular correlation coefficient, while not constrained to a uniform distribution , is very complex, and thus cannot be easily characterized the way we have done for the COOP.

2) was generated using a random number generator (rand) that provides a uniform distribution of at least 106 random values in MATLAB.

Four uniformly distributed random numbers (e.g., rw, rdo, rdl, and rb) are also generated between 0 and 1 at the beginning of every cycle for each bound kinesin.

First, uniformly distributed random numbers ral and 1312 are generated for each unbound kinesin.

The occurrence of an unbinding event is determined by comparing the calculated probability with uniformly distributed random numbers.

At each sampled time interval during the stimulus train the probability of receiving a shock was sampled from a uniform distribution .

On each iteration the optimizer was called within a random multi-started overlay (RMsearch), with 100 starting points selected from a uniform distribution between the parameter bounds, in order to reduce convergence on local minima.

The result is that consumption in the first time period, c1 could be selected at random from a uniform distribution , in which case, the expected consumption level, Cl, is close to 60 units.

We initialized the population of matrices by drawing their N - LD 2 terms from a uniform distribution .

We used goals with ranks 1, 2 and 3 whose terms were either 10 or 0 and then added a uniformly distributed noise in the range [0, 0.1] (o = 0.029).

We initialized the population of matrices and corresponding thresholds by drawing their N - LD(D + 1) terms from a uniform distribution in the range [-2,+2].

In the parenthesis, we report the classification performance when the class labels are uniformly distributed (maximum entropy).

The “null” and “dataset” baselines correspond to the base prediction accuracy in the case where the classes are uniformly distributed for each classification task, or the most representative class based on the data (highest prior) for each classification case is selected, respectively.

The “null” and “dataset” baselines correspond to the base prediction accuracy in the case Where the classes are uniformly distributed for each classification task, or the most representative class based on the data (highest prior) for each classification case is selected, respectively.