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  2. Bootstrapping (statistics) - Wikipedia

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

    Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy ( bias, variance, confidence intervals, prediction error, etc.) to sample estimates.

  3. Bootstrapping (compilers) - Wikipedia

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

    Bootstrapping (compilers) In computer science, bootstrapping is the technique for producing a self-compiling compiler – that is, a compiler (or assembler) written in the source programming language that it intends to compile. An initial core version of the compiler (the bootstrap compiler) is generated in a different language (which could be ...

  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. Bootstrapping - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping

    In computer technology, the term bootstrapping refers to language compilers that are able to be coded in the same language. (For example, a C compiler is now written in the C language. Once the basic compiler is written, improvements can be iteratively made, thus pulling the language up by its bootstraps).

  6. Particle filter - Wikipedia

    en.wikipedia.org/wiki/Particle_filter

    Particle filtering uses a set of particles (also called samples) to represent the posterior distribution of a stochastic process given the noisy and/or partial observations. The state-space model can be nonlinear and the initial state and noise distributions can take any form required.

  7. Self-modifying code - Wikipedia

    en.wikipedia.org/wiki/Self-modifying_code

    Self-modifying code is quite straightforward to implement when using assembly language. Instructions can be dynamically created in memory (or else overlaid over existing code in non-protected program storage), in a sequence equivalent to the ones that a standard compiler may generate as the object code.

  8. 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 ...

  9. Monte Carlo method - Wikipedia

    en.wikipedia.org/wiki/Monte_Carlo_method

    The approximation of a normal distribution with a Monte Carlo method. Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle.