Determination of intramolecular distance distribution functions using the "spectroscopic ruler". 1. Theoretical feasibility

1993 ◽  
Vol 26 (5) ◽  
pp. 1144-1151 ◽  
Author(s):  
Guojun Liu
Keyword(s):  
Distribution Functions ◽  
Distance Distribution ◽  
Intramolecular Distance

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.

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