Histogram, defined

Finally, the concentrated localized AE events are represented on the monitor in the form of a histogram "number of localized AE events vs axial coordinate", which is automatically updated at user-defined time intervals. 

Off-line analysis of stored data review of the stored data, organize data in different presentation windows, plot AE and plant parameters data so as to enable comparison and coirelation with the possibility to present data (histogram of AE events vs position, plant parameters and/or AE parameters vs time) conditioned in terms of time interval (initial time, final time) and/or position interval (defined portion of the component = initial coordinate, final coordinate) and/or plant parameters intervals (one or more plant parameters = initial value, final value). 

The mean can be evaluated from the classified data of the histogram it measures the center of the distribution. The mean (whose symbol is an overbar) is defined as... 

While the F-N curve is a cumulative illustration, the risk profile shows the expected frequency of accidents of a particular category or level of consequence. The diagonal line is a line of constant risk defined such that the product of expected frequency and consequence is a constant at each point along the line. " As the consequences of accidents go up, the expected frequency should go down in order for the risk to remain constant. As the example illustrates, if a portion of the histogram sticks its head up above the line (i.e., a particular type of accident contributes more than its fair share of the risk), then that risk is inconsistent with the risk presented by other accident types. (Note There is no requirement that you use a line of constant risk other more appropriate risk criteria for your application can be easily defined and displayed on the graph.)... 

Step 1. From a histogram of the data, partition the data into N components, each roughly corresponding to a mode of the data distribution. This defines the Cj. Set the parameters for prior distributions on the 6 parameters that are conjugate to the likelihoods. For the normal distribution the priors are defined in Eq. (15), so the full prior for the n components is... 

The differential and eumulative size distributions are elearly related, as shown in Figure 1.9. Differentiating the eumulative distribution restores the original histogram but in a smoother form. Two important properties ean be defined, the modal and median sizes. 

The overall x-range (which scales the plot) and that part of the x-range, which is to be subdivided into B classes (bins) can be individually defined essentially, this means that the plotted window can be adjusted to be the same for comparing several histograms, while bins need only be defined in that part of the x-axis where the measurements are concentrated. The optimal number of bins is suggested as B -Vn, but can be adjusted. 

Figure 22.4 Monte Carlo techniques were used to simulate different hypothetical individuals for different instances of the trial design, using variability and uncertainty distributions from the model analysis. The result is a collection of predicted outcomes, shown as a binned histogram (top figure). Success was defined as a difference in end point measurement of X or smaller between drug and comparator. Likelihood of success (shown in the bottom figure as a cumulative probability) for this example (low/medium drug dose and high comparator dose) is seen to be low, about 33%.

Formation of the mono- and dinitrosyl complexes is a thermodynamically favorable process, which distinctly depends on the electronic configuration of the metal center. The adsorption energy, defined as A=. Eaddukt - ( mzsm-5+ no)> is shown in the form of a histogram in Figure 2.16. Formation of mononitrosyl complexes is exothermic... 

Among the methods discussed in this book, FEP is the most commonly used to carry out alchemical transformations described in Sect. 2.8 of Chap. 2. Probability distribution and TI methods, in conjunction with MD, are favored if there is an order parameter in the system, defined as a dynamical variable. Among these methods, ABF, derived in Chap. 4, appears to be nearly optimal. Its accuracy, however, has not been tested critically for systems that relax slowly along the degrees of freedom perpendicular to the order parameter. Adaptive histogram approaches, primarily used in Monte Carlo simulations - e.g., multicanonical, WL and, in particular, the transition matrix method - yield superior results in applications to phase transitions,... 

Fig. 11 Dependence of AGo = Gp - Go on the electrode potential upon adsorption of different anions. Gp is defined by a Gaussian fit to the conductance peak closest to Go in the respective conductance histograms...

For the moment estimates, we have seen that the composition PDF, /, (delta functions (i.e., the empirical PDF in (6.210)). However, it should be intuitively apparent that this representation is unsatisfactory for understanding the behavior of fyiir) as a function of fj. In practice, the delta-function representation is replaced by a histogram using finite-sized bins in composition space (see Fig. 6.5). The histogram h, (k) for the /ctli cell in composition space is defined by... 

As mentioned in the previous paragraphs, to define an atomic environment they used the maximum gap rule. The Brunner-Schwarzenbach method was considered, in which all interatomic distances between an atom and its neighbours are plotted in a histogram such as those shown in Fig. 3.17. The height of the bars is proportional to the number of neighbours, and all distances are expressed as reduced values relative to the shortest distance. In the specific case of CsCl, having a = 411.3 pm,... 

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