Distribution function probability

It is instructive to see this in temis of the canonical ensemble probability distribution function for the energy, NVT - Referring to equation B3.3.1 and equation (B3.3.2I. it is relatively easy to see that... 

In either case, first-order or continuous, it is usefiil to consider the probability distribution function for variables averaged over a spatial block of side L this may be the complete simulation box (in which case we... 

C3.3.5.2 EXTRACTING THE ENERGY TRANSFER PROBABILITY DISTRIBUTION FUNCTION P(E, E)... 

Figure C3.3.11. The energy transfer probability distribution function P(E, E ) (see figure C3.3.2) for two molecules, pyrazine and hexafluorobenzene, excited at 248 nm, arising from collisions with carbon dioxide...

The probability distribution functions shown in figure C3.3.11 are limited to events that leave the bath molecule vibrationally unexcited. Nevertheless, we know that the vibrations of the bath molecule are excited, albeit with low probability in collisions of the type being considered here. Figure C3.3.12 shows how these P(E, E ) distribution... 

Figure C3.3.12. The energy-transfer-probability-distribution function P(E, E ) (see figure C3.3.2 and figure C3.3.11) for two molecules, pyrazine and hexafluorobenzene, excited at 248 nm, arising from collisions with carbon dioxide molecules. Both collisions that leave the carbon dioxide bath molecule in its ground vibrationless state, OO O, and those that excite the 00 1 vibrational state (2349 cm ), have been included in computing this probability. The spikes in the distribution arise from excitation of the carbon dioxide bath 00 1 vibrational mode.

We propose to describe the distribution of the number of fronts crossing x by the Poisson distribution function, discussed in Sec. 1.9. This probability distribution function describes the probability P(F) of a specific number of fronts F in terms of that number and the average number F as follows [Eq. (1.38)] ... 

Figure 1.7 Plots of (a) the radial wave function (b) the radial probability distribution function and (c) the radial charge density function 4nr Rl( against p...

Random Measurement Error Third, the measurements contain significant random errors. These errors may be due to samphng technique, instrument calibrations, and/or analysis methods. The error-probability-distribution functions are masked by fluctuations in the plant and cost of the measurements. Consequently, it is difficult to know whether, during reconciliation, 5 percent, 10 percent, or even 20 percent adjustments are acceptable to close the constraints. 

Modeling the pore size in terms of a probability distribution function enables a mathematical description of the pore characteristics. The narrower the pore size distribution, the more likely the absoluteness of retention. The particle-size distribution represented by the rectangular block is the more securely retained, by sieve capture, the narrower the pore-size distribution. 

Mathematica hasthisfunctionandmanyothersbuiltintoitssetof "add-on" packagesthatare standardwiththesoftware.Tousethemweloadthepackage "Statistics NormalDistribution The syntax for these functions is straightforward we specify the mean and the standard deviation in the normal distribution, and then we use this in the probability distribution function (PDF) along with the variable to be so distributed. The rest of the code is self-evident. 

The probability distribution of a randoni variable concerns tlie distribution of probability over tlie range of tlie random variable. The distribution of probability is specified by the pdf (probability distribution function). This section is devoted to general properties of tlie pdf in tlie case of discrete and continuous nmdoiii variables. Special pdfs finding e.xtensive application in liazard and risk analysis are considered in Chapter 20. 

Property 1 indicates tliat tlie pdf of a discrete random variable generates probability by substitution. Properties 2 and 3 restrict the values of f(x) to nonnegative real niunbers whose sum is 1. An example of a discrete probability distribution function (approaching a normal distribution - to be discussed in tlie next chapter) is provided in Figure 19.8.1. 

Figure 19.8.6. A continuous cumulative probability distribution function.

The probability distribution of a random variable concerns the distribution of probability over tlie range of the random variable. Tlie distribution of probability is specified by the pdf (probability distribution function). 

The Boltzman probability distribution function P may be written either in a discrete energy representation or in a continuous phase space formulation. 

In its more advanced aspects, kinetic theory is based upon a description of the gas in terms of the probability of a particle having certain values of coordinates and velocity, at a given time. Particle interactions are developed by the ordinary laws of mechanics, and the results of these are averaged over the probability distribution. The probability distribution function that is used for a given macroscopic physical situation is determined by means of an equation, the Boltzmann transport equation, which describes the space, velocity, and time changes of the distribution function in terms of collisions between particles. This equation is usually solved to give the distribution function in terms of certain macroscopic functions thus, the macroscopic conditions imposed upon the gas are taken into account in the probability function description of the microscopic situation. 

Fw is the force on particle i due to particle j, and is assumed to depend only upon the distance between the particles the prime on the summation means that the term j — i is omitted. The i -particle probability distribution function is defined as follows ... 

This is the probability of finding particle 1 with coordinate rx and velocity vx (within drx and dVj), particle 2 with coordinate r2 and velocity v2 (within phase space with velocity rather than momentum for convenience since only one type of particle is being considered, this causes no difficulties in Liouville s equation.) The -particle probability distribution function ( < N) is... 

The conditional probability distribution function of the random variables fa, , fa given that the random variables fa, , fa+m have assumed the values xn+1, , xn+m respectively, can be defined, in most cases of interest to us, by means of the following procedure. To simplify the discussion, we shall only present the details of the derivation for the case of two random variables fa and fa. We begin by using the definition, Eq. (3-159), to write... 

There are many ways we could assign probability distribution functions to the increments N(t + sk) — N(t + tk) and simultaneously satisfy the independent increment requirement expressed by Eq. (3-237) however, if we require a few additional properties, it is possible to show that the only possible probability density assignment is the Poisson process assignment defined by Eq. (3-231). One example of such additional requirements is the following50... 

These conditions automatically exclude from consideration exactly those time functions X(t) that we have been concerned with in this chapter namely, functions that have nontrivial (not simply a unit jump at the origin) probability distribution functions, and, therefore, do not go to zero as t - oo as required by either Eq. (3-301) or Eq. (3-302). These integrability conditions can be waived in favor of weaker ones that do not require the functions involved to go to zero as t - + oo by making use of a representation in the form of a Stieltjes-like integral66... 

Assume that a manufacturer gains a units on sold items and loses j8 units for unsold items. Also assume that the demand for y items is given by a probability distribution function f(y) with maximum... 

In a continuous game both the choice of strategy and the payoff as a function of that choice are continuous. The latter is particularly important because a discontinuous payoff function may not yield a solution. Thus, instead of a matrix [ow], a function M(x,y) gives the payoff each time a strategy is chosen (i.e., the value of x and y are fixed). The strategy of each player in this case is defined as a member of the class D of probability distribution functions that are defined as continuous, real-valued, monotonic functions such that... 

Normal product of free-field creation and annihilation operators, 606 Normal product operator, 545 operating on Fermion operators, 545 N-particle probability distribution function, 42... 

Under specific circumstances, alternative forms for ks j have been proposed like the parabolic or the truncated Gaussian probability distribution function for example [154]. 

Sinee the probability distribution functions (1) and (2) are even functions of Xia and Xg, respectively, the cross-term integral containing Xm and Xe to the first power will vanish.)... 

It will be assumed for the moment that the non-bonded atoms will pass each other at the distance Tg (equal to that found in a Westheimer-Mayer calculation) if the carbon-hydrogen oscillator happens to be in its average position and otherwise at the distance r = Vg + where is a mass-sensitive displacement governed by the probability distribution function (1). The potential-energy threshold felt is assumed to have the value E 0) when = 0 and otherwise to be a function E(Xja) which depends on the variation of the non-bonded potential V with... 

Abstract Wavefront sensing for adaptive optics is addressed. The most popular wavefront sensors are described. Restoring the wavefront is an inverse problem, of which the bases are explained. An estimator of the slope of the wavefront is the image centroid. The Cramer-Rao lower bound is evaluated for several probability distribution function... 

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