Probability density function example

The structure of a multiphase medium can be specified by the spatial distribution of the phases that form the medium. This spatial distribution can be generally represented by the phase function or, in specific situations, by the equivalent pore-network diagram, by the spatial distribution of particles or other constituents, and by the probability density function. Examples of the representation of the porous media are shown in Fig. 2. 

We conclude this section with examples of some particularly important probability density functions that will be used in later applications. In each of these examples, the reader should verify that the function px is a probability density function by showing that it is non-negative and has unit area. All of the integrals and sums involved are elementary except perhaps in the case of the gaussian distribution, for which the reader is referred to Cramer.7... 

The next example will illustrate the technique of calculating moments when the probability density function contains Dirac delta functions. The mean of the Poisson distribution, Eq. (3-29), is given by... 

The last example brings out very clearly that knowledge of only the mean and variance of a distribution is often not sufficient to tell us much about the shape of the probability density function. In order to partially alleviate this difficulty, one sometimes tries to specify additional parameters or attributes of the distribution. One of the most important of these is the notion of the modality of the distribution, which is defined to be the number of distinct maxima of the probability density function. The usefulness of this concept is brought out by the observation that a unimodal distribution (such as the gaussian) will tend to have its area concentrated about the location of the maximum, thus guaranteeing that the mean and variance will be fairly reasdnable measures of the center and spread of the distribution. Conversely, if it is known that a distribution is multimodal (has more than one... 

To describe single-point measurements of a random process, we use the first-order probability density function p/(/). Then p/(/) df is the probability that a measurement will return a result between / and / -I- df. We can characterize a random process by its moments. The nth moment is the ensemble average of /", denoted (/"). For example, the mean is given by the first moment of the probability density function. 

The shape of the probability density function, depends on the system. Some examples are shown in Fig. 4-4. This figure also contains probability density of age (see Section 4.2.3). Figure 4-4a might correspond to a lake with inlet and outlet on opposite sides of the lake. Most water molecules will then have a residence time in the lake roughly equal to the time it takes for the mean current to carry the water from the... 

A model must be introduced to simulate fast chemical reactions, for example, flamelet, or turbulent mixer model (TMM), presumed mapping. Rodney Eox describes many proposed models in his book [23]. Many of these use a probability density function to describe the concentration variations. One model that gives reasonably good results for a wide range of non-premixed reactions is the TMM model by Baldyga and Bourne [24]. In this model, the variance of the concentration fluctuations is separated into three scales corresponding to large, intermediate, and small turbulent eddies. 

Principles of mathematical modelling 2 Probability density function 112 Process control examples 505-524 Product inhibition 643, 649 Production rate in mass balance 27 Profit function 108 Proportional... 

The new model has also been applied to the calculation of thermally averaged probability density functions for the out-of-plane inversion motion of the CH and H3O ions [9]. Such probability densities can be obtained experimentally by means of Coulomb Explosion Imaging (CEI) techniques (see, for example, Refs. [10,11]), and the results in Ref. [9] will be useful in the interpretation of the resulting images, just as analogous calculations of the bending probability distribution for the CHj ion were instrumental in the interpretation of its CEI images (see Refs. [9,12] and references therein). 

Formally, suppose we have a random variable, jc, which has measurements over the range a to b. Also, assume that the probability density function of x can be written as p(x). In addition, assume a second function g, such that g(x ) p(x) =J x). For example, gix) could represent a dose-response function on concentration and p(x) is the probability density function of concentration. The expected value (which is the most likely value or the mean value) of g(J ) isp... 

Parameter Two distinct definitions for parameter are used. In the first usage (preferred), parameter refers to the constants characterizing the probability density function or cumulative distribution function of a random variable. For example, if the random variable W is known to be normally distributed with mean p and standard deviation o, the constants p and o are called parameters. In the second usage, parameter can be a constant or an independent variable in a mathematical equation or model. For example, in the equation Z = X + 2Y, the independent variables (X, Y) and the constant (2) are all parameters. 

Table 1. Basic Quantities in Analyses of CW Laser Scattering for Probability Density Function. In Eq. 1 within the table, F(J) is the photon count distribution obtained over a large number of consecutive short periods. For example, F(3) expresses the fraction of periods during which three photons are detected. The PDF, P(x), characterizes the statistical behavior of a fluctuating concentration. Eq. 1 describes the relationship between Fj and P(x) provided that the effects of dead time and detector imperfections such as multiple pulsing can be neglected. In order to simplify notation, the concentration is expressed in terms of the equivalent average number of counts per period, x. The normalized factorial moments and zero moments of the PDF can be shown to be equal by substitution of Eq.l into Eq.2. The relationship between central and zero moments is established by expansion of (x-a)m in Eq.(4). The trial PDF [Eq.(5)] is composed of a sum of k discrete concentration components of amplitude Ak at density xk. [The functions 5 (x-xk) are delta functions.]...

Probability density function (pdf) Indicates the relative likelihood of the different possible values of a random variable. For a discrete random variable, say X, the pdf is a function, say /, such that for any value x, /(x) is the probability that X = X. For example, if X is the number of pesticide applications in a year, then /(2) is the probability density function at 2 and equals the probability that there are two pesticide applications in a year. For a continuous random variable, say Y, the pdf is a function, say g, such that for any value y, g(y) is the relative likelihood that Y = y,0 < g y), and the integral of g over the range of y from minus infinity to plus infinity equals 1. For example, if Y is body weight, then g(70) is the probability density function for a body weight of 70 and the relative likelihood that the body weight is 70. Furthermore, if g 70)/g(60) = 2, then the body weight is twice as likely to be 70 as it is to be 60 (Sielken, Ch. 8). 

With turbulent combustion viewed as a random (or stochastic) process, mathematical bases are available for addressing the subject. A number of textbooks provide introductions to stochastic processes (for example, [55]). In turbulence, any stochastic variable, such as a component of velocity, temperature, or the concentration of a chemical species, which we might call v, is a function of the continuous variables of space x and time t and is, therefore, a stochastic function. A complete statistical description of a stochastic function would be provided by a probability-density functional, tf, defined by stating that the probability of finding the function in a small range i (x, t) about a particular function v(x, t) is [t (x, t)]<3t (x, t) ... 

An avenue that has received exploration is the development of equations for evolution of probability-density functions. If, for example, attention is restricted entirely to particular, fixed values of x and t, then the variable whose value may be represented by v becomes a random variable instead of a random function, and its statistics are described by a probability-density function. The probability-density function for v may be denoted by P(v where P(v) dv is the probability that the random variable lies in the range dv about the value v. By definition P(v) > 0, and P(v) dv = 1, One approach to obtaining an equation of evolution for P(v) is to introduce the ensemble average of a fine-grained density, as described by O Brien in [27], for example another is formally to perform suitable integrations in... 

Because of these difficulties with moment methods for reacting flows, we shall not present them here. A number of reviews are available [22], [25], [27], [32]. There are classes of turbulent combustion problems for which moment methods are reasonably well justified [40]. Since the computational difficulties in use of moment methods tend to be less severe than those for many other techniques (for example, techniques involving evolution equations for probability-density functions), they currently are being applied to turbulent combustion in relatively complex geometrical configurations [22], [31], [32]. Many of the aspects of moment methods play important roles in other approaches, notably in those for turbulent diffusion flames (Section 10.2). We shall develop those aspects later, as they are needed. 

Distributions like those in Figure 10.4, for example, indicate that Yp or T differs from Yp(Z) or T(Z), respectively. If mixing were complete in the sense that all probability-density functions were delta functions and fluctuations vanished, then differences like T — T Z) would be zero. That this situation is not achieved in turbulent diffusion flames has been described qualitatively by the term unmixedness [7]. Although different quantitative definitions of unmixedness have been employed by different authors, in one way or another they all are measures of quantities such as Yp — Yp(Z) or T — T(Z). The unmixedness is readily calculable from P(Z), given any specific definition (see Bilger s contribution to [27]). 

When the possible outcomes of a probability experiment are continuous (for example, the position of a particle along the x-axis) as opposed to discrete (for example, flipping a coin), the distribution of results is given by the probability density function P(x). The product P(x)dx gives the probability that the result falls in the interval of width dx centered about the value x. The first condition, that the probability density must be normalized, ensures that probability density is properly defined (see Appendix A6), and that all possible outcomes are included. This condition is expressed mathematically as... 

Fig. 14. Example of a probability density function analysis of a natural fault cluster illustrating a relationship between the largest offsets present and the number and size of smaller offset groups in the cluster.

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