Probability distributions parameters

In an ensemble of collisions, the impact parameters are distributed randomly on a disc with a probability distribution P(b) that is defined by P(b) db = 2nb db. The cross section da is then defined by... 

By including characteristic atomic properties, A. of atoms i andj, the RDF code can be used in different tasks to fit the requirements of the information to be represented. The exponential term contains the distance r j between the atoms i andj and the smoothing parameter fl, which defines the probability distribution of the individual distances. The function g(r) was calculated at a number of discrete points with defined intervals. 

The Bayesian alternative to fixed parameters is to define a probability distribution for the parameters and simulate the joint posterior distribution of the sequence alignment and the parameters with a suitable prior distribution. How can varying the similarity matrix... 

As described above, the code "SIFTING" requires several microstructural inputs in order to ealculate a failure probability distribution. We are thus able to assess the physieal soundness of the Burchell model by determining the change in the predicted distribution when microstructural input parameters, such as particle or pore size, are varied in the "SIFTING" code. Each microstructural input parameter... 

FIG. 3 Probability distributions of chain lengths at four temperatures (given as parameter) [28]. In the inset, oscillations of the MWD due to the formation of rings are shown for T = 0.2. 

When the underlying distribution is not known, tools such as histograms, probability curves, piecewise polynomial approximations, and general techniques are available to fit distributions to data. It may be necessary to assume an appropriate distribution in order to obtain the relevant parameters. Any assumptions made should be supported by manufacturer s data or data from the literature on similar items working in similar environments. Experience indicates that some probability distributions are more appropriate in certain situations than others. What follows is a brief overview on their applications in different environments. A more rigorous discussion of the statistics involved is provided in the CPQRA Guidelines. ... 

Recall lliat if Z has a log-nonnal distribution, tlien In Z lias a nonnal distribution with mean p and standard deviation g equal to the parameters in tlie pdf of Z. This fact, in conjunction witli tlie specified 5 and 95 percentiles of llie probability distribution of Z, can be used to obtain the values of p and a, and tliereby the parameters of tlie log-nonnal pdf of Z. If Z denotes the failure rate per year, tlie fact tliat tlie 5" percentile of the distribution of Z is 1/57 implies... 

For each of the 36 bus sections tliat had not already failed, the Weibull distribution was used to detennine tlie probability of failure before tlie next outage. Under assumption (a), tliis probability is P(T < 3301T > 209) i.e., tlie conditional probability of failure before 330 days, given tliat tlie bus section lias survived 209 days. Under assumption (b), tlie corresponding probability is P(T < 330 T > 230). For part (b), tlie estimates of the Weibull distribution parameters used in part (a) were modified to take into consideration tlie absence of failures for 3 additional weeks. 

The line the data supports on a hazard plot determines engineering information relating to the distribution of time to failure. Fan failure data and simulated data are illustrated here to explain how the information is obtained. The methods provide estimates of distribution parameters, percentiles, and probabilities of failure. The methods that give estimates of distribution parameters differ slightly from the hazard paper of one theoretical distribution to another and are given separately for each distribution. The methods that give estimates of distribution percentiles and failure probabilities are the same for all papers and are given first. 

If estimated of distribution parameters are desired from data plotted on a hazard paper, then the straight line drawn through the data should be based primarily on a fit to the data points near the center of the distribution the sample is from and not be influenced overly by data points in the tails of the distribution. This is suggested because the smallest and largest times to failure in a sample tend to vary considerably from the true cumulative hazard function, and the middle times tend to lie close to it. Similar comments apply to the probability plotting. 

The standard way to answer the above question would be to compute the probability distribution of the parameter and, from it, to compute, for example, the 95% confidence region on the parameter estimate obtained. We would, in other words, find a set of values h such that the probability that we are correct in asserting that the true value 0 of the parameter lies in 7e is 95%. If we assumed that the parameter estimates are at least approximately normally distributed around the true parameter value (which is asymptotically true in the case of least squares under some mild regularity assumptions), then it would be sufficient to know the parameter dispersion (variance-covariance matrix) in order to be able to compute approximate ellipsoidal confidence regions. 

To apply the above method, we must decide the distribution of parameter values to explore. One immediate answer would be to impose on the parameters an appropriate joint probability distribution, but this would require us to know it, or at least to have a reasonable idea of what it might be. 

Uncertainties in amounts of products to be manufactured Qi, processing times %, and size factors Sij will influence the production time tp, whose uncertainty reflects the individual uncertainties that can be presented as probability distributions. The distributions for shortterm uncertainties (processing times and size factors) can be evaluated based on knowledge of probability distributions for the uncertain parameters, i.e. kinetic parameters and other variables used for the design of equipment units. The probability of not being able to meet the total demand is the probability that the production time is larger than the available production time H. Hence, the objective function used for deterministic design takes the form ... 

When are the simplified results valid If the work path has buffer regions at its beginning and end during which the work parameter is fixed for a time > Tshort, then the subsystem will have equilibrated at the initial and final values of p in each case. Hence the odd work vanishes because TL 0, and the probability distribution reduces to Boltzmann s. 

It is only natural to consider ways that would allow us to use our knowledge of the whole distribution P0(AU), rather than its lew-AU tail only. The simplest strategy is to represent the probability distribution as an analytical function or a power-series expansion. This would necessarily involve adjustable parameters that could be determined primarily from our knowledge of the function in the well-sampled region. Once these parameters are known, we can evaluate the function over the whole domain of interest. In a way, this approach to modeling P0(AU) constitutes an extrapolation strategy. 

Here, V is vector notation for the set of all component energies Vy, and A, j gives the coefficient of Vy in the ith run. The Ay, without subscript i, indicate the values of A in the target ensemble. The histograms collected in the runs are multidimensional in that they are tabulated as functions of the component energies as well as the order parameter . Similarly, the final result of the WHAM calculation is a multidimensional probability distribution in V J and . 

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