Gaussian growth-disorder models and optical transform methods

1982 ◽  
Vol 38 (6) ◽  
pp. 761-772 ◽  
Author(s):  
T. R. Welberry ◽  
C. E. Carroll
Keyword(s):  
Transform Methods ◽  
Growth Disorder ◽  
Optical Transform

Direct Structure Determination by Optical-Transform Methods

Nature ◽  
1957 ◽  
Vol 180 (4583) ◽  
pp. 431-432
Author(s):  
M. M. CROWDER ◽  
K. A. MORLEY ◽  
C. A. TAYLOR
Keyword(s):  
Structure Determination ◽  
Transform Methods ◽  
Optical Transform

Optical transform methods 1939–1969

1970 ◽  
Vol 26 (2) ◽  
pp. 194-196
Author(s):  
G. Harburn ◽  
C. A. Taylor
Keyword(s):  
Optical Diffraction ◽  
X Ray Diffraction ◽  
Transform Methods ◽  
X Ray ◽  
Original Idea ◽  
The Many ◽  
Complex Phenomena ◽  
Physical Principles ◽  
Optical Transform

Sir Lawrence Bragg's flair for identifying the essential physical principles underlying the most complex phenomena is exemplified by his introduction of optical diffraction studies as a means of solving X-ray diffraction problems. This paper reviews briefly some of the many developments that have stemmed from his original idea.

OR11,62 The effect of genotype on molecular mechanisms leading to the 3-M primordial growth disorder

2010 ◽  
Vol 20 ◽  
pp. S28-S29
Author(s):  
T. Coulson ◽  
P. Murray ◽  
D. Hanson ◽  
G. Black ◽  
A. Whatmore ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Growth Disorder

scholarly journals Computation of Compound Distributions I: Aliasing Errors and Exponential Tilting

1999 ◽  
Vol 29 (2) ◽  
pp. 197-214 ◽  
Author(s):  
Rudolf Grübel ◽  
Renate Hermesmeier
Keyword(s):  
Numerical Evaluation ◽  
Change Of Measure ◽  
Exponential Tilting ◽  
Transform Methods ◽  
Compound Distributions ◽  
Heavy Tailed Distributions ◽  
Panjer Recursion ◽  
Heavy Tailed ◽  
Suitable Change

AbstractNumerical evaluation of compound distributions is one of the central numerical tasks in insurance mathematics. Two widely used techniques are Panjer recursion and transform methods. Many authors have pointed out that aliasing errors imply the need to consider the whole distribution if transform methods are used, a potential drawback especially for heavy-tailed distributions. We investigate the magnitude of aliasing errors and show that this problem can be solved by a suitable change of measure.

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