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  2. Pearson correlation coefficient - Wikipedia

    en.wikipedia.org/wiki/Pearson_correlation...

    In the "non-parametric" bootstrap, n pairs (x i, y i) are resampled "with replacement" from the observed set of n pairs, and the correlation coefficient r is calculated based on the resampled data. This process is repeated a large number of times, and the empirical distribution of the resampled r values are used to approximate the sampling ...

  3. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5] and get [2,5,4,4,1]), so, assuming N is sufficiently large, for all practical purposes there is virtually zero probability that it will be identical to the original "real" sample. This process is repeated a large ...

  4. Bootstrap aggregating - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_aggregating

    v. t. e. Bootstrap aggregating, also called bagging (from b ootstrap agg regat ing ), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting.

  5. Expectation–maximization algorithm - Wikipedia

    en.wikipedia.org/wiki/Expectation–maximization...

    Expectation–maximization algorithm. In statistics, an expectation–maximization ( EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. [1] The EM iteration alternates between performing an ...

  6. Resampling (statistics) - Wikipedia

    en.wikipedia.org/wiki/Resampling_(statistics)

    The best example of the plug-in principle, the bootstrapping method. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio ...

  7. Out-of-bag error - Wikipedia

    en.wikipedia.org/wiki/Out-of-bag_error

    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 ...

  8. Liouville field theory - Wikipedia

    en.wikipedia.org/wiki/Liouville_field_theory

    Using the conformal bootstrap approach, Liouville theory can be shown to be the unique conformal field theory such that. the spectrum is a continuum, with no multiplicities higher than one, the correlation functions depend analytically on and the momenta, degenerate fields exist. Lagrangian formulation

  9. Hercules–Corona Borealis Great Wall - Wikipedia

    en.wikipedia.org/wiki/Hercules–Corona_Borealis...

    The Hercules–Corona Borealis Great Wall (HCB) or simply the Great Wall is a galaxy filament that is the largest known structure in the observable universe, measuring approximately 10 billion light-years in length (the observable universe is about 93 billion light-years in diameter).