Simple Forms of the Bivariate Density Function

2009 ◽  
pp. 351-400
Author(s):  
N. Balakrishna ◽  
Chin Diew Lai
Keyword(s):  
Density Function ◽  
Bivariate Density

Decomposition of Bivariate Symmetric Density Function

1996 ◽  
Vol 46 (1-2) ◽  
pp. 129-134
Author(s):  
Takashi Sadao Tomizawa Seo ◽  
Jun-Ichiro Minaguchi
Keyword(s):  
Density Function ◽  
Contingency Tables ◽  
Marginal Homogeneity ◽  
Symmetric Density ◽  
Symmetry Model ◽  
Bivariate Density

For the analysis of square contingency tables, it is known that the symmetry model holds if and only if both the quasi-symmetry and the marginal homogeneity models hold (Caussinus, 1965; Bishop et al., 1975, p. 287). This paper shows that a similar decomposition for bivariate density function (instead of cell probabilities) holds.

Decomposition of Bivariate Point-Symmetric Density Function

1998 ◽  
Vol 48 (1-2) ◽  
pp. 21-28
Author(s):  
Sadao Tomizawa ◽  
Tomono Konuma
Keyword(s):  
Density Function ◽  
Point Symmetry ◽  
Symmetric Density ◽  
Symmetry Model ◽  
Bivariate Density

For the analysis of contigency tables, it is known that the point-symmetry model holds if and only if both the quasi-point symmetry and the marginal point-symmetry models hold (Tomizawa, 1985). This paper shows that a similar decomposition for bivariate density function (instead of cell probabilities) holds.

Simulation of Dispersal of Sockeye Salmon (Oncorhynchus nerka) Underyearlings in Babine Lake

1977 ◽  
Vol 34 (9) ◽  
pp. 1379-1388 ◽  
Author(s):  
S. E. Simms ◽  
P. A. Larkin
Keyword(s):  
Computer Program ◽  
Density Function ◽  
Sockeye Salmon ◽  
Oncorhynchus Nerka ◽  
Lake Basin ◽  
Density Functions ◽  
Directional Bias ◽  
Natural Movement ◽  
Reflection Principles ◽  
Bivariate Density

The movement of underyearling sockeye salmon (Oncorhynchus nerka) in Babine Lake was computer simulated using a grid array with reflection principles applied for the complex shoreline boundaries. Dispersal was in accord with bivariate density functions summed appropriately to give 25 discrete components, with the standard deviations related to observed swimming speeds. Directionally biased dispersal was simulated using the lognormal density function. Natural movement of sockeye underyearlings in Babine Lake suggests three seasonal periods: (1) May 25–June 12; (2) July 13–August 28; (3) August 29–October 12. For (1), natural movement is best simulated by assuming that 90% of the fish move with a 10:90 bias toward southward movement, while 10% of the fish move at random; for (2), the best assumptions are that 50% of the fish move with a 90:10 bias for northward movement and 50% move at random; for (3), the best simulation assumed that 100% of the fry move at random. Considering the simplicity of the model assumptions, the fit to the data for natural movements is good. To construct a better predictive model the directional bias mimicked by the lognormal density function should be divided into components related to physical circulation in the lake basin and components related to fry behavior. The architecture of the computer program is briefly described and could be applied to other salmon dispersal studies. Key words: sockeye salmon, simulation, natural movements, fry distribution

The structure of amorphous and polycrystalline materials using energy-filtered RDF analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1190-1191
Author(s):  
David Cockayne ◽  
David McKenzie
Keyword(s):  
Electron Diffraction ◽  
Diffraction Pattern ◽  
Density Function ◽  
Electron Diffraction Pattern ◽  
Polycrystalline Materials ◽  
Nearest Neighbour ◽  
X Ray ◽  
Selected Area Electron Diffraction ◽  
Reduced Density ◽  
System F

The technique of Electron Reduced Density Function (RDF) analysis has ben developed into a rapid analytical tool for the analysis of small volumes of amorphous or polycrystalline materials. The energy filtered electron diffraction pattern is collected to high scattering angles (currendy to s = 2 sinθ/λ = 6.5 Å-1) by scanning the selected area electron diffraction pattern across the entrance aperture to a GATAN parallel energy loss spectrometer. The diffraction pattern is then converted to a reduced density function, G(r), using mathematical procedures equivalent to those used in X-ray and neutron diffraction studies.Nearest neighbour distances accurate to 0.01 Å are obtained routinely, and bond distortions of molecules can be determined from the ratio of first to second nearest neighbour distances. The accuracy of coordination number determinations from polycrystalline monatomic materials (eg Pt) is high (5%). In amorphous systems (eg carbon, silicon) it is reasonable (10%), but in multi-element systems there are a number of problems to be overcome; to reduce the diffraction pattern to G(r), the approximation must be made that for all elements i,j in the system, fj(s) = Kji fi,(s) where Kji is independent of s.

Analytical Representation of the Density Function of Normal Distribution of Noise

2015 ◽  
Vol 47 (8) ◽  
pp. 24-40 ◽  
Author(s):  
Telman Abbas ogly Aliev ◽  
Naila F. Musaeva ◽  
Matanat Tair kyzy Suleymanova ◽  
Bahruz Ismail ogly Gazizade
Keyword(s):  
Normal Distribution ◽  
Density Function ◽  
Analytical Representation

Technology for Calculating the Parameters of the Density Function of the Normal Distribution of the Useful Component in a Noisy Process

2016 ◽  
Vol 48 (4) ◽  
pp. 39-55 ◽  
Author(s):  
Telman Abbas ogly Aliev ◽  
Naila Fuad kyzy Musaeva ◽  
Matanat Tair kyzy Suleymanova ◽  
Bahruz Ismail ogly Gazizade
Keyword(s):  
Normal Distribution ◽  
Density Function
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