2010
DOI: 10.1119/1.3254017
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Drawing an elephant with four complex parameters

Abstract: We define four complex numbers representing the parameters needed to specify an elephantine shape. The real and imaginary parts of these complex numbers are the coefficients of a Fourier coordinate expansion, a powerful tool for reducing the data required to define shapes.

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Cited by 131 publications
(80 citation statements)
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“…3(b) in the Comment). Even just a few parameters are enough to fit an elephant [8]! And second, the authors' fitting method is not a physical theory since it has no predictive power at all −precisely because of its dependence on many adjustable parameters.…”
mentioning
confidence: 99%
“…3(b) in the Comment). Even just a few parameters are enough to fit an elephant [8]! And second, the authors' fitting method is not a physical theory since it has no predictive power at all −precisely because of its dependence on many adjustable parameters.…”
mentioning
confidence: 99%
“…A famous quote among physicists, attributed to John von Neumann, is 'with four parameters I can fit an elephant, and with five I can make it wiggle its trunk' [3]. Joe Howard and colleagues actually wrote such an elephant-fitting simulation in a very enjoyable recent paper [4], and, because you wondered, with a fifth parameter the trunk does indeed wiggle. This means that, provided enough tweakable parameters are introduced, a model simulation can in principle fit any behavior.…”
Section: Meaningful Models -Any Model Is Not Better Than No Modelmentioning
confidence: 96%
“…Annotated code for the procedure described above can be found in a repository at https:// github.com/dispersing/2DKernSim/blob/master/2DKern Sim.R. For the sake of balancing model overfitting (sensu Mayer et al 2010) and biological meaningfulness, deterministic model selection was limited to two exponential functions that have been shown to have biologically meaningful parameters (see Wichmann et al 2009;Bullock et al 2011). For both pdfs ðxðrÞ; f ðrÞÞ we chose the following models:…”
Section: Seed Dispersalmentioning
confidence: 99%