Determination of nonequilibrium distribution functions from nonlinear fluctuation-dissipation relations

1980 ◽  
Vol 23 (12) ◽  
pp. 947-951 ◽  
Author(s):  
G. N. Bochkov ◽  
Yu. E. Kuzovlev
Keyword(s):  
Distribution Functions ◽  
Nonequilibrium Distribution ◽  
Fluctuation Dissipation ◽  
Nonlinear Fluctuation

scholarly journals Delineating the Polarized and Unpolarized Parton Structure of the Nucleon

2015 ◽  
Vol 37 ◽  
pp. 1560053
Author(s):  
Pedro Jimenez-Delgado
Keyword(s):  
Parton Distribution ◽  
Distribution Functions ◽  
Distribution Analysis ◽  
Coupling Constant ◽  
Parton Distribution Functions ◽  
Careful Treatment ◽  
Future Data ◽  
The Impact

Reports on our latest extractions of parton distribution functions of the nucleon are given. First an overview of the recent JR14 upgrade of our unpolarized PDFs, including NNLO determinations of the strong coupling constant and a discussion of the role of the input scale in parton distribution analysis. In the second part of the talk recent results on the determination of spin-dependent PDFs from the JAM collaboration are reported, including a careful treatment of hadronic and nuclear corrections, as well as reports on the impact of present and future data in our understanding of the spin of the nucleon.

Microwave determination of the distribution functions of electrons in low current discharges

1966 ◽  
Vol 42 (1) ◽  
pp. 93-99
Author(s):  
P. Bocchieri ◽  
B. Crosignani ◽  
G. Siragusa
Keyword(s):  
Distribution Functions

scholarly journals Multivariate Distributions in Hydrology and River Regulation

1976 ◽  
Vol 7 (5) ◽  
pp. 265-280
Author(s):  
N.A. Kartvelishvili ◽  
L.T. Gottschalk
Keyword(s):  
Markov Process ◽  
Finite Difference ◽  
Difference Scheme ◽  
River Runoff ◽  
Finite Difference Scheme ◽  
River Regulation ◽  
Distribution Functions ◽  
Multivariate Distributions ◽  
Model Order

It is assumed that the river runoff process can be approximated by a Markov process. The process is thus described by M distribution functions: Fn (qt, t ; qt-1; t-1;…;qt-n, t-n), t ≡ 1, 2, …, M where M is the number of time intervals within the year, n - the order of the Markov process and qt, in general, is a vector representing runoff at several sites in a river or neighbouring rivers. Fundamental hypothesis of relations between multivariate distributions and corresponding marginal distributions is given. A finite difference scheme for multisite and multilag generation of river runoff is derived. The derivation is based on the multivariate normal distribution. Different methods for determination of the order of the finite difference scheme are discussed as well as the influence of model order and method of parameter estimation on properties of the model.

scholarly journals Statistical Uncertainties of Space Plasma Properties Described by Kappa Distributions

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 541
Author(s):  
Georgios Nicolaou ◽  
George Livadiotis
Keyword(s):  
Accurate Determination ◽  
Space Plasma ◽  
High Energy ◽  
Distribution Functions ◽  
Kappa Distribution ◽  
Plasma Properties ◽  
Electrostatic Analyzer ◽  
Bulk Parameters ◽  
Plasma Bulk

The velocities of space plasma particles often follow kappa distribution functions, which have characteristic high energy tails. The tails of these distributions are associated with low particle flux and, therefore, it is challenging to precisely resolve them in plasma measurements. On the other hand, the accurate determination of kappa distribution functions within a broad range of energies is crucial for the understanding of physical mechanisms. Standard analyses of the plasma observations determine the plasma bulk parameters from the statistical moments of the underlined distribution. It is important, however, to also quantify the uncertainties of the derived plasma bulk parameters, which determine the confidence level of scientific conclusions. We investigate the determination of the plasma bulk parameters from observations by an ideal electrostatic analyzer. We derive simple formulas to estimate the statistical uncertainties of the calculated bulk parameters. We then use the forward modelling method to simulate plasma observations by a typical top-hat electrostatic analyzer. We analyze the simulated observations in order to derive the plasma bulk parameters and their uncertainties. Our simulations validate our simplified formulas. We further examine the statistical errors of the plasma bulk parameters for several shapes of the plasma velocity distribution function.

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