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Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.
A bootstrap paradox, also known as an information loop, an information paradox, [6] an ontological paradox, [7] or a "predestination paradox" is a paradox of time travel that occurs when any event, such as an action, information, an object, or a person, ultimately causes itself, as a consequence of either retrocausality or time travel.
Bootstrap methods are great for inference, but bootstrap aggregation is a method for ensemble learning - i.e. to aggregate collections of models, for robust development using subsamples of the data. To include bagging into bootstrapping is to misunderstand the use of bagging.
Semantic bootstrapping is a linguistic theory of language acquisition which proposes that children can acquire the syntax of a language by first learning and recognizing semantic elements and building upon, or bootstrapping from, that knowledge. [8] According to Pinker, [8] semantic bootstrapping requires two critical assumptions to hold true:
Each row of points is a sample from the same normal distribution. The colored lines are 50% confidence intervals for the mean, μ.At the center of each interval is the sample mean, marked with a diamond.
One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement. The out-of-bag set is all data not chosen in the sampling process. When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each ...
In the sense used in this paragraph, bootstrapping an operational amplifier means "using a signal to drive the reference point of the op-amp's power supplies". [5] A more sophisticated use of this rail bootstrapping technique is to alter the non-linear C/V characteristic of the inputs of a JFET op-amp in order to decrease its distortion. [6] [7]
In phylogenetics, parsimony is mostly interpreted as favoring the trees that minimize the amount of evolutionary change required (see for example [2]).Alternatively, phylogenetic parsimony can be characterized as favoring the trees that maximize explanatory power by minimizing the number of observed similarities that cannot be explained by inheritance and common descent.