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K-distribution

About: K-distribution is a research topic. Over the lifetime, 1281 publications have been published within this topic receiving 51774 citations.


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Book ChapterDOI
01 Jan 2017
TL;DR: In this paper, the use of two-, three-, and five-point estimates to create useful probability distributions are explained and demonstrated in this chapter. But, the authors do not discuss the problem of not using probabilities in decision-making.
Abstract: Lack of data is a common excuse from managers for not using probabilities in decision-making. The object of this chapter is to demonstrate how to derive probability distributions from managerial experience. The use of two-, three-, and five-point estimates to create useful probability distributions are explained and demonstrated in this chapter.

1 citations

Journal ArticleDOI
Barrow1
TL;DR: In a follow-up article as mentioned in this paper, the authors pointed out that the results tell us nothing about the shape of the distribution (contrary to the claim by Ratkowsky et al. that they imply a gamma distribution with an a of 150), and that the coefficient of variation does not depend upon temperature.
Abstract: Sir—I would like this considered for publication as a comment on the 1996 paper by Ratkowsky and colleagues. I have no reason to quarrel with the experiment or its results, but I believe the authors have taken one or two false steps in drawing conclusions. Specifically, the results tell us nothing about the shape of the distribution (contrary to the claim by Ratkowsky et al. that they imply a gamma distribution with an a of 150). What the results do say is that the coefficient of variation does not depend upon temperature.

1 citations

Proceedings ArticleDOI
06 Dec 1982
TL;DR: This paper presents a three-activity approach to fitting distributions to data and highlights the capabilities of UNIFIT which allow the analyst to perform these activities in a thorough and timely manner.
Abstract: An important problem which occurs in many different disciplines is that of determining a probability distribution which is a good representation of an observed data set. For example, in building a simulation model of a manufacturing process or of a computer system, one needs to determine appropriate probability distributions for the input random variables. A common solution to this problem is to fit standard distributions (e.g., normal or gamma) to observed system data. However, since this fitting process is rather complicated and time consuming when done by hand, it is often performed in a superficial and incorrect manner. The net effect is, of course, that the selected distributions may not be good representations of the observed data.UNIFIT is a state-of-the-art, interactive computer package for fitting probability distributions to observed data. By combining the latest statistical techniques with graphical displays, the package allows one to perform a comprehensive analysis of a data set in significantly less time than would otherwise be possible. It employs a there-activity approach for determining an appropriate distribution. The first activity involves using heuristic techniques such as histograms or sample moments to hypothesize one or more families of distributions which might be representative of the observed data. For example, if our data are continuous and if a histogram of the data indicates that the density function of the underlying distribution is skewed to the right, then we might hypothesize that a gamma, lognormal, or Weibull distribution is an appropriate model for our observed data. However, each of these families of distributions has several parameters which must be specified in order to have a completely determined distribution. Therefore, the second activity typically involves estimating the parameters of each hypothesized family from the data, thereby specifying a number of particular distributions. In the third activity we determine which of the fitted distributions, if any, is the best representation for the data using both heuristic techniques and goodness-of-fit tests. An example of a heuristic technique provided by UNIFIT is the frequency comparison, which is a graphical display showing both the observed proportion of observations and the expected proportion of observations from a particular fitted distribution for each histogram interval. The frequency comparison is particularly useful for visually determining how well a selected probability model represents the underlying distribution for the data. In addition to heuristic techniques, UNIFIT makes available to an analyst the chi-square, the Kolmogorov-Smirnov, and the Anderson-Darling goodness-of-fit tests. These tests can be considered to be a formal approach for detecting gross discrepancies between the fitted distribution and the observed data.

1 citations

Posted Content
TL;DR: In this paper, the probability density functions of net-proton multiplicity distributions are constructed from the beam energy scan results of the STAR experiment using the Pearson curve method for two different transverse momentum windows.
Abstract: The probability density functions of net-proton multiplicity distributions are constructed from the Beam Energy Scan results of the STAR experiment using the Pearson curve method for two different transverse momentum windows. The $6^{th}$ and $8^{th}$ order cumulants of net-proton multiplicity distributions are estimated from the constructed probability density functions. The beam energy dependence of $C_{6}/C_{2}$ and $C_{8}/C_{2}$ are found to be sensitive to the acceptance window. This method provides a unique opportunity to study the O(4) criticality near the chiral crossover transition and estimating the higher-order cumulants. In general, it is useful to determine the probability density function uniquely of a frequency data if the first four cumulants are known.

1 citations

Journal ArticleDOI
TL;DR: In this paper, a necessary and sufficient condition is given for the existence of stationary probability distributions of a non-Markovian model with linear transition rule, which has applications in psychological and biological research, in control theory, and in adaptation theory.

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20232
20228
20213
20207
201914
201816