Several colloidal systems, that are of practical importance, contain spherically symmetric particles the size of which changes continuously. Polydisperse fluid mixtures can be described by a continuous probability density of one or more particle attributes, such as particle size. Thus, they may be viewed as containing an infinite number of components. It has been several decades since the introduction of polydispersity as a model for molecular mixtures [73], but only recently has it received widespread attention [74-82]. Initially, work was concentrated on nearly monodisperse mixtures and the polydispersity was accounted for by the construction of perturbation expansions with a pure, monodispersive, component as the reference fluid [77,80]. Subsequently, Kofke and Glandt [79] have obtained the equation of state using a theory based on the distinction of particular species in a polydispersive mixture, not by their intermolecular potentials but by a specific form of the distribution of their chemical potentials. Quite recently, Lado [81,82] has generalized the usual OZ equation to the case of a polydispersive mixture. Recently, the latter theory has been also extended to the case of polydisperse quenched-annealed mixtures [83,84]. As this approach has not been reviewed previously, we shall consider it in some detail. [Pg.154]

Continuous distribution functions Some experiments, such as liquid chromatography or mass spectrometry, allow for the determination of continuous or quasi-continuous distribution functions, which are readily obtained by a transition from the discrete property variable X to the continuous variable X and the replacement of the discrete statistical weights g, by the continuous probability density g(X). For simplicity, we assume g(X) as being normalized J ° g(X)dX = 1. Averages and moments of a quantity Y(X) are defined by analogy to the discrete case as... [Pg.210]

Note that the expression in (3.1) is a continuous probability distribution in that p(U T)dU gives the probability of macrostates with energy U dU/2. In an NVT simulation, we measure this distribution to a finite precision by employing a nonzero bin width All. Letting f(U) be the number of times an energy within the range [U,U I All] is visited in the simulation, the normalized observed energy distribution... [Pg.78]

The information entropy of a probability distribution is defined as S[p(] = — p, In ph where p, forms the set of probabilities of a distribution. For continuous probability distributions such as momentum densities, the information entropy is given by. S yj = - Jy(p) In y(p) d3p, with an analogous definition in position space... [Pg.68]

RET can be used to investigate the lateral organization of phospholipids (range of 100 A) in gel and fluid phases. Indeed, information can be obtained on the probe heterogeneity distribution the donors sense various concentrations of acceptor according to their localization. A continuous probability function of having donors with a mean local concentration CA of acceptors in their surroundings should thus be introduced in Eq. (9.36) written in two dimensions ... [Pg.262]

There are two different ways of representing uncertainty. The first approach is the continuous probability distribution where numerical integration is employed over the random continuous probability space. This approach maintains the model size but on the other hand introduces nonlinearities and computational difficulties to the problem. The other approach is the scenario-based approach where the random space is considered as discrete events. The main disadvantage of this approach is the substantial increase in computational requirements with an increase in the number ofuncertain parameters. The discrete distribution with a finite number K of possible... [Pg.183]

The classical, frequentist approach in statistics requires the concept of the sampling distribution of an estimator. In classical statistics, a data set is commonly treated as a random sample from a population. Of course, in some situations the data actually have been collected according to a probability-sampling scheme. Whether that is the case or not, processes generating the data will be snbject to stochastic-ity and variation, which is a sonrce of uncertainty in nse of the data. Therefore, sampling concepts may be invoked in order to provide a model that accounts for the random processes, and that will lead to confidence intervals or standard errors. The population may or may not be conceived as a finite set of individnals. In some situations, such as when forecasting a fnture value, a continuous probability distribution plays the role of the popnlation. [Pg.37]

The question how a discrete distribution can be approximated by a continuous probability is answered by (7.5) it is a coarse scale description. More precisely, (7.7) gives the probability of finding Y in an interval y,y + Ay when Ay > 1. It obviously incorrectly describes the probability in an interval Ay 1. [Pg.27]

The complete results are in Table XIV which shows the development of phenolic compound content during maceration. The anthocyanins increase until about the sixth day and then decrease. The tannins, on the other hand, increase continually, probably because they are more abundant, especially after the seeds are added. However, in some cases peculiar to vintages low in tannins, the production of these compounds is the same as that of anthocyanin. [Pg.87]

The random variable, Y, as defined above has the chi distribution, which is described by the following continuous probability density function, fix, N) ... [Pg.152]

One first needs the basic notions associated with a continuous probability distribution. Consider the age or the retention time of a molecule in the compartment as a random variable, A. Let ... [Pg.210]

It is useful to distinguish two forms of probability distribution, discontinuous and continuous. As an example of a discontinuous distribution consider the outcome of throwing a die. The chance outcome of a series of throws can be represented as a discontinuous probability distribution. There is only a limited number of possible outcomes and the results can be shown in the form of a block histogram. The second kind of distribution would arise in, e.g., replicate measurements of the fluorescence intensity of a sample. These observations will differ as a result of statistical variation, as discussed in the previous section, but instead of being a single chance event as in the case of the die, many chance factors will contribute to the observed variation in the fluorescence data. The variation observed in this case is an example of a continuous probability distribution. Although it is true in principle... [Pg.299]

For the simple experiment on throwing a die, P is the same for all x possible outcomes and is equal to 1/6. For continuous probability distributions, we choose a range between x and x + Ax in which the observation must fall dx can in principle be very small, but in practice the magnitude of dx is determined by experimental limitations of the measurement or equipment. [Pg.300]

Standard spreadsheet calculation results are entered for all chemicals and pathways to be modeled following the methods used for deterministic calculations. For each of the random variables, discrete or continuous probability density functions are placed in the appropriate cells. [Pg.2791]

We therefore turned to the evaluation of the information discrimination AS, defined below for a continuous probability function Pk(x)... [Pg.13]

Normal Distribution is a continuous probability distribution that is useful in characterizing a large variety of types of data. It is a symmetric, bell-shaped distribution, completely defined by its mean and standard deviation and is commonly used to calculate probabilities of events that tend to occur around a mean value and trail off with decreasing likelihood. Different statistical tests are used and compared the y 2 test, the W Shapiro-Wilks test and the Z-score for asymmetry. If one of the p-values is smaller than 5%, the hypothesis (Ho) (normal distribution of the population of the sample) is rejected. If the p-value is greater than 5% then we prefer to accept the normality of the distribution. The normality of distribution allows us to analyse data through statistical procedures like ANOVA. In the absence of normality it is necessary to use nonparametric tests that compare medians rather than means. [Pg.329]

Q.4.11 What is the difference between discrete and continuous probability distributions ... [Pg.16]

A.4.11 Discrete probability distributions model systems with finite, or countably infinite, values, while a continuous probability distribution model systems with infinite possible values within a range. [Pg.19]

For a stochastic simulation replicated many times, it would be inefficient to analyze every single trial one by one. Besides, in a stochastic simulation, the analyst is not interested in what happens with a single trial but what happens in the long run. Recall that the probability of observing a sample drawn from a continuous probability distribution is zero that is, the probability of observing the number... [Pg.857]

In the next steps which could also be thermally induced, the aromatic systems (3080, 1600, and 1515 cm-1) decompose, while acetylenes (3320 and 2255 cm-1) are formed. At the same time isocyanate species are detected (2270 cm-1), which decompose upon further irradiation, or by reaction with other species (e.g., water to form amines). This decomposition is at least partially thermal, because at low repetition rates (0.086 Hz as compared to 10 Hz) the decrease of the isocyanate band is less pronounced. In the following steps nitrile (2230 cm-1) and aliphatic hydrocarbons (CH) are formed (2950 cm-1), as shown in Fig. 65. The increase of the peak area of the aliphatic CH compounds is slower and nearly linear with pulse numbers, suggesting that these species are formed continuously, probably through combination reactions. The volatile products detected by mass spectrometry and... [Pg.172]

The amount of end-use products containing SCCPs was estimated using the following equation that represents Weibull distribution that is a continuous probability distribution and is often used in the field of life data analysis due to its flexibility. [Pg.161]

Having related the discrete and continuous probability we can likewise relate the probability current jk t) of the discrete system to the probability current J x,t) of the continuous envelope description. According to the relation between the discrete and continuous probability eq. (2.20), the discrete probability current t) from fc to fc + 1 is equal to the continuous probability current t x = k + (see Fig. 2.7),... [Pg.57]

The continuous probability current J x,t) is related to the probability distribution density V x,t) by the continuity equation... [Pg.57]

For a normalized probability distribution, the probability that x lies in the infinitesimal interval x,x + dx) is f x) dx, which is the probability per unit length times the length of the infinitesimal interval. The fact that /(jc) is a probability per unit length is the reason for using the name probability density for it. Since all continuously variable values of x in some range are possible, a continuous probability distribution must apply to a set of infinitely many members. Such a set is called the population to which the distribution applies. The probability f(x )dx is the fraction of the population that has its value of x lying in the region between x and x + dx. [Pg.148]

Figure 2.1 The mode (xj), the median (xj), and the mean (m) for a continuous probability distribution function. |

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