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For example, a mechanistic model could be used to identify areas that are clearly outside the species’ fundamental niche, and these areas can be marked as absences or excluded from analysis. See [6] for a comparison between mechanistic and correlative models.
Sparse distributed memory (SDM) is a mathematical model of human long-term memory introduced by Pentti Kanerva in 1988 while he was at NASA Ames Research Center. [1]This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines – e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas ...
Direct coupling analysis or DCA is an umbrella term comprising several methods for analyzing sequence data in computational biology. [1] The common idea of these methods is to use statistical modeling to quantify the strength of the direct relationship between two positions of a biological sequence, excluding effects from other positions.
The expected phenotypic correlation is the bivariate heritability' and can be calculated as the square roots of the heritabilities multiplied by the genetic correlation. (Using a Plomin example, [38] for two traits with heritabilities of 0.60 & 0.23, =, and phenotypic correlation of r=0.45 the bivariate heritability would be =, so of the ...
For example, psychology is one of these areas. Computational models allow researchers in psychology to enhance their understanding of the functions and developments in nervous systems. Examples of methods include the modelling of neurons, networks and brain systems and theoretical analysis. [ 38 ]
The growing ability of neuroscientists to manipulate neurons using methods from molecular biology in combination with optical tools (e.g., Adamantidis et al. 2007) depends on the simultaneous development of appropriate behavioral assays and model organisms amenable to large-scale genomic analysis and manipulation. The combination of fine ...
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In statistics, path analysis is used to describe the directed dependencies among a set of variables. This includes models equivalent to any form of multiple regression analysis, factor analysis, canonical correlation analysis, discriminant analysis, as well as more general families of models in the multivariate analysis of variance and covariance analyses (MANOVA, ANOVA, ANCOVA).