scholarly journals Tract-Based Probability Densities of Diffusivity Measures in DT-MRI

2010 ◽  
pp. 542-549 ◽  
Author(s):  
Çağatay Demiralp ◽  
David H. Laidlaw
Keyword(s):  
Probability Densities

Binary decompositions of probability densities and random-bit simulation

2020 ◽  
Vol 26 (2) ◽  
pp. 163-169
Author(s):  
Vladimir Nekrutkin
Keyword(s):  
New York ◽  
Distribution Function ◽  
Random Number ◽  
Discrete Distribution ◽  
Random Number Generation ◽  
Academic Press ◽  
Bernoulli Trials ◽  
Algorithms And Complexity ◽  
Binary Decomposition ◽  
Probability Densities

AbstractThis paper is devoted to random-bit simulation of probability densities, supported on {[0,1]}. The term “random-bit” means that the source of randomness for simulation is a sequence of symmetrical Bernoulli trials. In contrast to the pioneer paper [D. E. Knuth and A. C. Yao, The complexity of nonuniform random number generation, Algorithms and Complexity, Academic Press, New York 1976, 357–428], the proposed method demands the knowledge of the probability density under simulation, and not the values of the corresponding distribution function. The method is based on the so-called binary decomposition of the density and comes down to simulation of a special discrete distribution to get several principal bits of output, while further bits of output are produced by “flipping a coin”. The complexity of the method is studied and several examples are presented.

scholarly journals Towards the Dependence on Parameters for the Solution of the Thermostatted Kinetic Framework

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 59
Author(s):  
Bruno Carbonaro ◽  
Marco Menale
Keyword(s):  
Complex System ◽  
Continuous Dependence ◽  
Social Role ◽  
Probability Distributions ◽  
Functional Dependence ◽  
Transition Probability ◽  
Classical Mechanics ◽  
Human Society ◽  
Wide Range ◽  
Probability Densities

A complex system is a system involving particles whose pairwise interactions cannot be composed in the same way as in classical Mechanics, i.e., the result of interaction of each particle with all the remaining ones cannot be expressed as a sum of its interactions with each of them (we cannot even know the functional dependence of the total interaction on the single interactions). Moreover, in view of the wide range of its applications to biologic, social, and economic problems, the variables describing the state of the system (i.e., the states of all of its particles) are not always (only) the usual mechanical variables (position and velocity), but (also) many additional variables describing e.g., health, wealth, social condition, social rôle ⋯, and so on. Thus, in order to achieve a mathematical description of the problems of everyday’s life of any human society, either at a microscopic or at a macroscpoic scale, a new mathematical theory (or, more precisely, a scheme of mathematical models), called KTAP, has been devised, which provides an equation which is a generalized version of the Boltzmann equation, to describe in terms of probability distributions the evolution of a non-mechanical complex system. In connection with applications, the classical problems about existence, uniqueness, continuous dependence, and stability of its solutions turn out to be particularly relevant. As far as we are aware, however, the problem of continuous dependence and stability of solutions with respect to perturbations of the parameters expressing the interaction rates of particles and the transition probability densities (see Section The Basic Equations has not been tackled yet). Accordingly, the present paper aims to give some initial results concerning these two basic problems. In particular, Theorem 2 reveals to be stable with respect to small perturbations of parameters, and, as far as instability of solutions with respect to perturbations of parameters is concerned, Theorem 3 shows that solutions are unstable with respect to “large” perturbations of interaction rates; these hints are illustrated by numerical simulations that point out how much solutions corresponding to different values of parameters stay away from each other as t→+∞.

scholarly journals Elasticity of connected semiflexible quadrilaterals

Soft Matter ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. 102-112
Author(s):  
Mohammadhosein Razbin ◽  
Alireza Mashaghi
Keyword(s):  
Thermal Fluctuations ◽  
Analytic Expressions ◽  
Probability Densities

The analytic expressions for the probability densities associated with the thermal fluctuations and the elasticity of the structure are obtained.

scholarly journals Random numbers from the tails of probability distributions using the transformation method

2013 ◽  
Vol 16 (2) ◽  
Author(s):  
Daniel Fulger ◽  
Enrico Scalas ◽  
Guido Germano
Keyword(s):  
Probability Distributions ◽  
Transformation Method ◽  
Fractional Diffusion ◽  
Random Numbers ◽  
Stochastic Solution ◽  
Probability Densities ◽  
Speed Up ◽  
Time Fractional Diffusion Equation ◽  
Transformation Methods ◽  
Very High

AbstractThe speed of many one-line transformation methods for the production of, for example, Lévy alpha-stable random numbers, which generalize Gaussian ones, and Mittag-Leffler random numbers, which generalize exponential ones, is very high and satisfactory for most purposes. However, fast rejection techniques like the ziggurat by Marsaglia and Tsang promise a significant speed-up for the class of decreasing probability densities, if it is possible to complement them with a method that samples the tails of the infinite support. This requires the fast generation of random numbers greater or smaller than a certain value. We present a method to achieve this, and also to generate random numbers within any arbitrary interval. We demonstrate the method showing the properties of the transformation maps of the above mentioned distributions as examples of stable and geometric stable random numbers used for the stochastic solution of the space-time fractional diffusion equation.

Recursive Estimates of Probability Densities

1969 ◽  
Vol 5 (3) ◽  
pp. 246-247 ◽  
Author(s):  
C. Wolverton ◽  
Terry Wagner
Keyword(s):  
Probability Densities

Nonparametric Roughness Penalties for Probability Densities

Biometrika ◽  
1971 ◽  
Vol 58 (2) ◽  
pp. 255 ◽  
Author(s):  
I. J. Good ◽  
R. A. Gaskins
Keyword(s):  
Probability Densities
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