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In population genetics, directional selection is a type of natural selection in which one extreme phenotype is favored over both the other extreme and moderate phenotypes. This genetic selection causes the allele frequency to shift toward the chosen extreme over time as allele ratios change from generation to generation.
The McDonald–Kreitman test [1] is a statistical test often used by evolutionary and population biologists to detect and measure the amount of adaptive evolution within a species by determining whether adaptive evolution has occurred, and the proportion of substitutions that resulted from positive selection (also known as directional selection).
The peacock tail in flight, the classic example of an ornament assumed to be a Fisherian runaway. Fisherian runaway or runaway selection is a sexual selection mechanism proposed by the mathematical biologist Ronald Fisher in the early 20th century, to account for the evolution of ostentatious male ornamentation by persistent, directional female choice.
The hypothetico-deductive model or method is a proposed description of the scientific method. According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable , using a test on observable data where the outcome is not yet known.
Initially, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner. [3] Algorithms that achieve this quickly became known as "boosting". Freund and Schapire's arcing (Adapt[at]ive Resampling and Combining), [7] as a general technique, is more or less synonymous with boosting. [8]
Directional statistics (also circular statistics or spherical statistics) is the subdiscipline of statistics that deals with directions (unit vectors in Euclidean space, R n), axes (lines through the origin in R n) or rotations in R n. More generally, directional statistics deals with observations on compact Riemannian manifolds including the ...
In the TE framework, the entailing and entailed texts are termed text (t) and hypothesis (h), respectively.Textual entailment is not the same as pure logical entailment – it has a more relaxed definition: "t entails h" (t ⇒ h) if, typically, a human reading t would infer that h is most likely true. [1]
The process discourages the analyst from choosing one "likely" hypothesis and using evidence to prove its accuracy. Cognitive bias is minimized when all possible hypotheses are considered. [1] Evidence – The analyst then lists evidence and arguments (including assumptions and logical deductions) for and against each hypothesis. [1]