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  2. Bootstrapping (statistics) - Wikipedia

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

    This method is known as the stationary bootstrap. Other related modifications of the moving block bootstrap are the Markovian bootstrap and a stationary bootstrap method that matches subsequent blocks based on standard deviation matching.

  3. Bootstrapping - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping

    In general, bootstrapping usually refers to a self-starting process that is supposed to continue or grow without external input. Many analytical techniques are often called bootstrap methods in reference to their self-starting or self-supporting implementation, such as bootstrapping (statistics), bootstrapping (finance), or bootstrapping (linguistics).

  4. Bootstrapping (finance) - Wikipedia

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

    In finance, bootstrapping is a method for constructing a (zero-coupon) fixed-income yield curve from the prices of a set of coupon-bearing products, e.g. bonds and swaps. [ 1 ] A bootstrapped curve , correspondingly, is one where the prices of the instruments used as an input to the curve, will be an exact output , when these same instruments ...

  5. 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 ...

  6. Bootstrap aggregating - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_aggregating

    Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms. It also reduces variance and overfitting.

  7. Temporal difference learning - Wikipedia

    en.wikipedia.org/wiki/Temporal_difference_learning

    Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods , and perform updates based on current estimates, like dynamic programming methods.

  8. What Does It Mean To Bootstrap a Business? - AOL

    www.aol.com/finance/does-mean-bootstrap-business...

    There are several ways to fund a small business including taking out a loan, applying for a grant and receiving capital from investors. Another alternative is bootstrapping. Here's what small ...

  9. Bootstrapping (electronics) - Wikipedia

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

    Bootstrapping is a technique in the field of electronics where part of the output of a system is used at startup. A bootstrap circuit is one where part of the output of an amplifier stage is applied to the input, so as to alter the input impedance of the amplifier.