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Causal approximations are based on the idea that more approximate descriptions usually explain less about a phenomenon than more accurate descriptions.
In this formalization, models are defined as sets of model fragments, causal explanations are generated using causal ordering, and model simplicity is based on the intuition that using more approximate descriptions of fewer phenomena leads to simpler models.
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The result shows that the α-μ model provides a more approximate description for the measured distributions.
It tells us to go for approximation — more approximate solutions, which find many right answers, but not all right answers.
High-level descriptions are usually much less information-dense but more approximate.
Elsewhere, though, that sense of involvement is more fleeting, and the response of singers to each other far more approximate.
Similar but nevertheless approximate descriptions of the former two components are available in literature.
More approximate and computationally accessible methods yield higher estimated errors.
These BLAST and clustering results provide approximate descriptions of gene family size distributions.
Therefore, approximate descriptions of the PDFs and nonlinear mapping process between parameter and model response space have to be used.
(These names are approximate descriptions. Lower color temperatures are warmer/yellower; higher temperatures are cooler/bluer).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com