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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.
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).
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 ...
The Preboot Execution Environment (PXE) was introduced as part of the Wired for Management [2] framework by Intel and is described in the specification published by Intel and SystemSoft. PXE version 2.0 was released in December 1998, and the update 2.1 was made public in September 1999. [3] The PXE environment makes use of several standard ...
Bootstrapping populations in statistics and mathematics starts with a sample {, …,} observed from a random variable.. When X has a given distribution law with a set of non fixed parameters, we denote with a vector , a parametric inference problem consists of computing suitable values – call them estimates – of these parameters precisely on the basis of the sample.
They can use different I/O protocols, but SPI is the most common. Unified Extensible Firmware Interface ( UEFI, / ˈjuːɪfaɪ / or as an acronym) [b] is a specification that defines the architecture of the platform firmware used for booting the computer hardware and its interface for interaction with the operating system.
Bootstrap error-adjusted single-sample technique. In statistics, the bootstrap error-adjusted single-sample technique ( BEST or the BEAST) is a non-parametric method that is intended to allow an assessment to be made of the validity of a single sample. It is based on estimating a probability distribution representing what can be expected from ...
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