scholarly journals Phases in the mixing of gases via the Ehrenfest urn model

2010 ◽  
Author(s):  
Srinivasan Balaji ◽  
Hosam M. Mahmoud
Keyword(s):  
Urn Model ◽  
Mixing Of Gases

An Urn Model and the Prediction of Order Statistics

1975 ◽  
Vol 4 (3) ◽  
pp. 245-250
Author(s):  
Kenneth Kaminsky ◽  
Eugene Luks ◽  
Paul Nelson
Keyword(s):  
Order Statistics ◽  
Urn Model

An Urn Model for the Multi-Sample Capture/Recapture Sequential Tagging Process.

1987 ◽  
Author(s):  
Jose G. Leite ◽  
Carlos A. De Braganca Pereira
Keyword(s):  
Urn Model ◽  
Capture Recapture

scholarly journals Random additions in urns of integers

2021 ◽  
Vol 58 (2) ◽  
pp. 335-346
Author(s):  
Mackenzie Simper
Keyword(s):  
Finite Group ◽  
Random Process ◽  
Uniform Distribution ◽  
Discrete Time ◽  
Random Variable ◽  
Urn Model ◽  
Infinite Type ◽  
The Mean ◽  
A Finite Group ◽  
Proof Of Convergence

AbstractConsider an urn containing balls labeled with integer values. Define a discrete-time random process by drawing two balls, one at a time and with replacement, and noting the labels. Add a new ball labeled with the sum of the two drawn labels. This model was introduced by Siegmund and Yakir (2005) Ann. Prob.33, 2036 for labels taking values in a finite group, in which case the distribution defined by the urn converges to the uniform distribution on the group. For the urn of integers, the main result of this paper is an exponential limit law. The mean of the exponential is a random variable with distribution depending on the starting configuration. This is a novel urn model which combines multi-drawing and an infinite type of balls. The proof of convergence uses the contraction method for recursive distributional equations.

scholarly journals Zipf’s, Heaps’ and Taylor’s Laws are Determined by the Expansion into the Adjacent Possible

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 752 ◽  
Author(s):  
Francesca Tria ◽  
Vittorio Loreto ◽  
Vito Servedio
Keyword(s):  
Ad Hoc ◽  
Ecological Systems ◽  
Dirichlet Processes ◽  
Urn Model ◽  
Modeling Framework ◽  
Self Consistent ◽  
Pólya’S Urn ◽  
Innovation Rate ◽  
Innovation Processes ◽  
Simple Modeling

Zipf’s, Heaps’ and Taylor’s laws are ubiquitous in many different systems where innovation processes are at play. Together, they represent a compelling set of stylized facts regarding the overall statistics, the innovation rate and the scaling of fluctuations for systems as diverse as written texts and cities, ecological systems and stock markets. Many modeling schemes have been proposed in literature to explain those laws, but only recently a modeling framework has been introduced that accounts for the emergence of those laws without deducing the emergence of one of the laws from the others or without ad hoc assumptions. This modeling framework is based on the concept of adjacent possible space and its key feature of being dynamically restructured while its boundaries get explored, i.e., conditional to the occurrence of novel events. Here, we illustrate this approach and show how this simple modeling framework, instantiated through a modified Pólya’s urn model, is able to reproduce Zipf’s, Heaps’ and Taylor’s laws within a unique self-consistent scheme. In addition, the same modeling scheme embraces other less common evolutionary laws (Hoppe’s model and Dirichlet processes) as particular cases.

scholarly journals Analytic study of the three-urn model for separation of sand

2004 ◽  
Vol 70 (3) ◽  
Author(s):  
G. M. Shim ◽  
B. Y. Park ◽  
J. D. Noh ◽  
Hoyun Lee
Keyword(s):  
Analytic Study ◽  
Urn Model

APPLICATIONS OF TWO NON-CENTRAL HYPERGEOMETRIC DISTRIBUTIONS OF BIASED SAMPLING STATISTICAL MODELS

2021 ◽  
Vol 4 (4) ◽  
pp. 415-424
Author(s):  
A. A. Issa ◽  
K. O. Adetunji ◽  
T. Alanamu ◽  
E. J. Adefila ◽  
K. A. Muhammed
Keyword(s):  
Statistical Models ◽  
Research Work ◽  
Hypergeometric Distribution ◽  
Biased Sampling ◽  
Inversion Method ◽  
Urn Model ◽  
Fisher Distribution ◽  
Direct Inversion ◽  
Mean And Variance ◽  
Hypergeometric Distributions

Statistical models of biased sampling of two non-central hypergeometric distributions Wallenius' and Fisher's distribution has been extensively used in the literature, however, not many of the logic of hypergeometric distribution have been investigated by different techniques. This research work examined the procedure of the two non-central hypergeometric distributions and investigates the statistical properties which includes the mean and variance that were obtained. The parameters of the distribution were estimated using the direct inversion method of hyper simulation of biased urn model in the environment of R statistical software, with varying odd ratios (w) and group sizes (mi). It was discovered that the two non - central hypergeometric are approximately equal in mean, variance and coefficient of variation and differ as odds ratios (w) becomes higher and differ from the central hypergeometric distribution with ω = 1. Furthermore, in univariate situation we observed that Fisher distribution at (ω = 0.2, 0.5, 0.7, 0.9) is more consistent than Wallenius distribution, although central hypergeometric is more consistent than any of them. Also, in multinomial situation, it was observed that Fisher distribution is more consistent at (ω = 0.2, 0.5), Wallenius distribution at (ω = 0.7, 0.9) and central hypergeometric at (ω = 0.2)    

A simple way to look at a Bayesian model for the statistics of earthquake prediction

1981 ◽  
Vol 71 (6) ◽  
pp. 1929-1931
Author(s):  
John G. Anderson
Keyword(s):  
Earthquake Prediction ◽  
Bayesian Model ◽  
Bayesian Models ◽  
Urn Model

Abstract A simple urn model is presented for earthquake prediction statistics. This model is equivalent to the Bayesian models of Collins (1977), Guagenti and Scirocco (1980), and Kijko (1981).

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