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A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.

Short for two dimensions. For example a map is a 2d model of the world.
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For questions asking about the meaning of abbreviations that are widely used in statistics.
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Absolute risk, is a synonym for incidence. This is a "measure of the rate at which people without a disease develop it during a specific period of time"
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Question concerns the consequences of using the absolute value function in part of the definition of one or more statistics.
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A/B testing, also known as split or bucket testing, is a controlled comparison of the effectiveness of variants of a website, email, or other commercial product.
The community pursuing research, education or scholarship, typically although not universally in universities.
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for accept-reject sampling methods. These are also known as rejection sampling methods. These methods sample a random variable from a dominating measure (h) and accepts the draw if an aux…
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Accuracy of an estimator is the degree of closeness of the estimates to the true value. For a classifier, accuracy is the proportion of correct classifications. (This second usage is not good practice…
The AutoCorrelation Function and Partial AutoCorrelation Function pertain to the correlation of a time series with itself at different lags. They are used to detect non-independence & suggest p, d, q …
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Active learning is a setting where an automated learning system can request labels from an external source, perhaps a human user or a real-world experiment. It is used to try to learn good models whil…
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Questions relating to financial risk; often, but not limited to, insurance. This includes questions regarding stochastic distribution of cash flows, probability of ruin, financial payments above a thr…
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A popular boosting algorithm (short for "adaptive boosting"). Boosting combines weakly predictive models into a strongly predictive model.
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AdaGrad (for adaptive gradient algorithm) is an enhanced stochastic gradient descent algorithm that automatically determines a per-parameter learning rate.
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An adaptive algorithm for gradient-based optimization of stochastic objective functions, often used to train deep neural networks.
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ambiguous and should be avoided.
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Admissible estimator: there is no other estimator for which the risk is $\leq$ for all possible true values of the target parameter.
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An adversarial example in a classification problem is a synthesized observation that is supposed to fool the classifier, such as a photo of a dog that has some cat characteristics and tricks a classif…
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Refers to "lumping together" potentially inhomogeneous groups of data.
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Agreement is the degree to which two raters, instruments, etc, give the same value when applied to the same object. Special statistical methods have been designed for this task.
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AIC stands for the Akaike Information Criterion, which is one technique used to select the best model from a class of models using a penalized likelihood. A smaller AIC implies a better model.
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Use for questions relating to algebraic statistics, the branch of statistical theory concerned with methods from abstract algebra, especially algebraic geometry and commutative algebra. This tag is no…
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