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Entropy 2015, 17(5), 2642-2654; doi:10.3390/e17052642

Stochastic Processes via the Pathway Model

1
Centre for Mathematical and Statistical Sciences, Peechi Campus, KFRI, Peechi-680653, Kerala, India
2
Department of Mathematics and Statistics, McGill University, 805 Sherbrooke Street West, Montreal, QC H3A2K6, Canada
3
Office for Outer Space Affairs, United Nations, P.O. Box 500, Vienna International Center, A-1400 Vienna, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Scarfone
Received: 11 March 2015 / Revised: 22 April 2015 / Accepted: 23 April 2015 / Published: 24 April 2015
(This article belongs to the Section Statistical Mechanics)
View Full-Text   |   Download PDF [319 KB, uploaded 24 April 2015]   |  

Abstract

After collecting data from observations or experiments, the next step is to analyze the data to build an appropriate mathematical or stochastic model to describe the data so that further studies can be done with the help of the model. In this article, the input-output type mechanism is considered first, where reaction, diffusion, reaction-diffusion, and production-destruction type physical situations can fit in. Then techniques are described to produce thicker or thinner tails (power law behavior) in stochastic models. Then the pathway idea is described where one can switch to different functional forms of the probability density function through a parameter called the pathway parameter. The paper is a continuation of related solar neutrino research published previously in this journal. View Full-Text
Keywords: data analysis; model building; input-output type stochastic models; thicker or thinner-tailed models; pathway idea; pathway model, solar neutrinos data analysis; model building; input-output type stochastic models; thicker or thinner-tailed models; pathway idea; pathway model, solar neutrinos
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mathai, A.M.; Haubold, H.J. Stochastic Processes via the Pathway Model. Entropy 2015, 17, 2642-2654.

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