Probability density function equation

We remark in passing that co,i(s) will also yield the Laplace transform of the characteristic function of the configuration space probability density function. Equations (241)-(243) then lead to the generalisation of the Gross-Sack result [39,40] for a fixed axis rotator to fractal time relaxation governed by Eq. (235), namely,... 

Reeks, M. W. 2005b On probability density function equations for particle dispersion in a uniform shear flow. Journal of Fluid Mechanics 522, 263-302. 

The shape of the Normal distribution is shown in Figure 3 for an arbitrary mean, /i= 150 and varying standard deviation, ct. Notice it is symmetrical about the mean and that the area under each curve is equal representing a probability of one. The equation which describes the shape of a Normal distribution is called the Probability Density Function (PDF) and is usually represented by the term f x), or the function of A , where A is the variable of interest or variate. 

In the introduction to this section, two differences between "classical" and Bayes statistics were mentioned. One of these was the Bayes treatment of failure rate and demand probttbility as random variables. This subsection provides a simple illustration of a Bayes treatment for calculating the confidence interval for demand probability. The direct approach taken here uses the binomial distribution (equation 2.4-7) for the probability density function (pdf). If p is the probability of failure on demand, then the confidence nr that p is less than p is given by equation 2.6-30. 

Introducing a concept of gradient diffusion for particles and employing a mixture fraction for the non-reacting fluid originating upstream, / = c Vc O) and a probability density function for the statistics of the fluid elements, /(/), equation (2.100) becomes... 

The properties of joint distribution functions can be stated most easily in terms of their associated probability density functions. The n + mth order joint probability density function px. . , ( > ) is defined by the equation... 

Equation (3-104) (sometimes called the stationarity property of a probability density function) follows from the definition of the joint distribution function upon making the change of variable t = t + r... 

Table 2.3 is used to classify the differing systems of equations, encountered in chemical reactor applications and the normal method of parameter identification. As shown, the optimal values of the system parameters can be estimated using a suitable error criterion, such as the methods of least squares, maximum likelihood or probability density function. 

Multiparticle collisions are carried out at time intervals x as described earlier. We can write the equation of motion for the phase space probability density function as a simple generalization of Eq. (15) by replacing the free-streaming operator with streaming in the intermolecular potential. We find... 

Werner Heisenberg stated that the exact location of an electron could not be determined. All measuring technigues would necessarily remove the electron from its normal environment. This uncertainty principle meant that only a population probability could be determined. Otherwise coincidence was the determining factor. Einstein did not want to accept this consequence ("God does not play dice"). Finally, Erwin Schrodinger formulated the electron wave function to describe this population space or probability density. This equation, particularly through the work of Max Born, led to the so-called "orbitals". These have a completely different appearance to the clear orbits of Bohr. 

Another approach to calculating AA relies on estimating the appropriate probability density functions. The connection between the probabilities of different states and the partition function is natural in statistical mechanics. Equation (1.19) is a reflection of this connection. Similarly, the probability of observing the potential energy of the system being equal to U is ... 

The importance of chemical-reaction kinetics and the interaction of the latter with transport phenomena is the central theme of the contribution of Fox from Iowa State University. The chapter combines the clarity of a tutorial with the presentation of very recent results. Starting from simple chemistry and singlephase flow the reader is lead towards complex chemistry and two-phase flow. The issue of SGS modeling discussed already in Chapter 2 is now discussed with respect to the concentration fields. A detailed presentation of the joint Probability Density Function (PDF) method is given. The latter allows to account for the interaction between chemistry and physics. Results on impinging jet reactors are shown. When dealing with particulate systems a particle size distribution (PSD) and corresponding population balance equations are intro-... 

In a first simple model for target detection it is assumed that the background clutter can be described by a statistical model in which the different range cells inside the sliding window contain statistically independent identically exponentially distributed (iid) random variables X i,..., X/v. The probability density function (pdf) of exponentially distributed clutter variables is fully described by the equation ... 

These differential equations depend on the entire probability density function / (x, t) for x(t). The evolution with time of the probability density function can, in principle, be solved with Kolmogorov s forward equation (Jazwinski, 1970), although this equation has been solved only in a few simple cases (Bancha-Reid, 1960). The implementation of practical algorithms for the computation of the estimate and its error covariance requires methods that do not depend on knowing p(x, t). 

Note that the RANS formulation used in (B.44) and (B.45) can easily be extended to the LES, as outlined in Section 5.10. Moreover, by following the same steps as outlined above, DQMOM can be used with the joint velocity, composition PDF transport equation. Finally, the reader can observe that the same methodology is applicable to more general distribution functions than probability density functions. Indeed, DQMOM can be applied to general population balance equations such as those used to describe multi-phase flows. 

Janicka, J., W. Kolbe, and W. Kollmann (1979). Closure of the transport equation for the probability density function of turbulent scalar fields. Journal of Non-Equilibrium Thermodynamics 4, 47-66. 

The temporal luminescence of a highly heterogeneous sensor-carrier mixtures cannot be uniquely represented by sums of exponentials (Eq. (9.23)) due to the lack of orthogonality of the exponential function. In this case it becomes appropriate to express equations (9.17) or (9.23) in terms of probability density functions or lifetime distribution functions 5t(8 14)... 

Considering the tracer entering the vessel at a given instant of time to be the nuclei formed at that time, C, (Do and C(0) can be converted to the number density of nuclei in the whole vessel, n, in the 1st tank, no and that of crystals in the exit stream from the vessel, n(0), respectively. The crystals having the residence time of 0 grow up to the size L, which is given by Equation 1. Therefore, by using Equations a-1 or a-2 and 1, the number basis probability density function of final product crystals, fn(L) is obtained, as follows. 

The weight basis probability density function of crystals in exit stream, fw(L) is derived from in a similar way. Multiplying both sides of Equation a-6 by PcfyL, where rc and fy are the density and volumetric shape factor of crystals, respectively,... 

Quantitative uncertainty analysis is not appropriate when in a worst-case approach, risk is found to be negligible when held evidence indicates obvious and severe effects when information is insufficient to adequately characterize the model equation, input probability density functions (PDFs), and the relationships between the PDFs or when it is more cost-effective to take action than to conduct more analyses. 

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. 

This model is directly derived from the Langmuir isotherm. It assumes that the adsorbent surface consists of two different types of independent adsorption sites. Under this assumption, the adsorption energy distribution can be modeled by a bimodal discrete probability density function, where two spikes (delta-Dirac functions) are located at the average adsorption energy of the two kinds of sites, respectively. The equation of the Bilangmuir isotherm is... 

The alternative is the use of a descriptive mathematical model without any relation with the solution of the transport equation. On the analog of the characterization of statistical probability density functions a peak shape f(t) can be characterized by moments, defined by ... 

A very important probability distribution is the normal or Gaussian distribution (after the German mathematician, Karl Friedrich Gauss, 1777-1855). The normal distribution has the same value for the mean, median and mode. The equation describing this distribution (the probability density function)... 

The basic issue is at a higher level of generality than that of the particular mechanical assumptions (Newtonian, quantum-theoretical, etc.) concerning the system. For simplicity of exposition, we deal with the classical model of N similar molecules in a closed vessel "K, intermolecular forces being conservative, and container forces having a force-function usually involving the time. Such a system is Hamiltonian, and we assume that the potentials are such that its Hamiltonian function is bounded below. The statistics of the system are conveyed by a probability density function 3F defined over the phase space QN of our Hamiltonian system. Its time evolution is completely determined by Liouville s equation... 

The first difficulty derives from the fact that given any values of the macroscopic expected values (restricted only by broad moment inequality conditions), a probability density always exists (mathematically) giving rise to these expected values. This means that as far as the mathematical framework of dynamics and probability goes, the macroscopic variables could have values violating the laws of phenomenological physics (e.g., the equation of state, Newton s law of heat conduction, Stokes law of viscosity, etc.). In other words, there is a macroscopic dependence of macroscopic variables which reflects nothing in the microscopic model. Clearly, there must exist a principle whereby nature restricts the class of probability density functions, SF, so as to ensure the observed phenomenological dependences. 

Here, we present an approach for the description of such anomalous transport processes that is based on the continuous-time random walk theory for a power-law waiting time distribution w(t) but which can be used to find the probability density function of the random walker in the presence of an external force field, or in phase space. This framework is fractional dynamics, and we show how the traditional kinetic equations can be generalized and solved within this approach. 

A chemical reaction, for instance, an intramolecular reaction R P, is viewed as a one-dimensional motion in the phase space of a particle in a double-well potential and undergoing Markovian random forces. The dynamics of the particle are described in terms of the Kramers equation (4.160), and the rate of reaction can, in principle, be calculated from the knowledge of the probability density function p(x0 t, x).16,147... 

Why are these equations represented by 4th order polynomials and not 2nd order curves given that the vertical variation of temperature and vapor fraction are well approximated by second order functions The simple answer is that the transition from condensing water vapor to liquid water above 0 °C to condensing water ice below -20 °C, and the attendant affect on the fractionation factor (Fig. 2), results in additional structure not captured by 2nd or 3rd order curves. Each of the equations fit their respective model output with an R2 > 0.9997. The lack of symmetry of the modeled uncertainty reflects asymmetry in the probability density function and particularly the long tail toward lower values of T relative to the mean (see Fig. 2 of Rowley et al. 2001). The effect of this long tail is well displayed in both Figure 5 and 7. 

Equation (10.40) is sometimes found in a simpler form at the cost of hiding the complexity of the terms involved. This form is based on the introduction of a probability density function for the reaction coordinate and the associated potential of mean force, in contrast to previously, where we considered the probability density of a particular arrangement of n atoms. Let I l(Q )d,Q be the probability of finding the reaction coordinate in the range Q, Q + dQ. Then, from equilibrium statistical mechanics (see Appendix A.2), the probability density function I l(Q ) is given by... 

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