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Otto announced Bootstrap 4 on October 29, 2014. [15] The first alpha version of Bootstrap 4 was released on August 19, 2015. [16] The first beta version was released on August 10, 2017. [17] Otto suspended work on Bootstrap 3 on September 6, 2016, to free up time to work on Bootstrap 4. Bootstrap 4 was finalized on January 18, 2018. [18]
Bootstrap Studio was launched on October 19, 2015 with a post on Product Hunt where it reached number 4 in the Product of the Day category. [5] Version 2.0 of the software was released on January 22, 2016 and brought JavaScript editing, multi-page support and improved the CSS support. [6] Version 4.0 was launched on November 1, 2017.
Bootstrap v1.1 Variables and mixins to bootstrap any new web development project. Modified from original version for Twitter Blueprint. Rewrite section / Structure and function
The first version of FuelPHP (FuelPHP 1.0) was developed under the GitHub repository named Fuel. Another GitHub repository named FuelPHP was created for the development of the second version (FuelPHP 2.0).
An improved version of the Vysochanskij-Petunin inequality for one-sided tail bounds exists. For a unimodal random variable X {\displaystyle X} with mean μ {\displaystyle \mu } and variance σ 2 {\displaystyle \sigma ^{2}} , and r ≥ 0 {\displaystyle r\geq 0} , the one-sided Vysochanskij-Petunin inequality [ 2 ] holds as follows:
A bootstrap paradox, also known as an information loop, an information paradox, [6] an ontological paradox, [7] or a "predestination paradox" is a paradox of time travel that occurs when any event, such as an action, information, an object, or a person, ultimately causes itself, as a consequence of either retrocausality or time travel. [8] [9 ...
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 ...
One problem is that, when g is not small, the confidence interval can blow up when using Fieller's theorem. Andy Grieve has provided a Bayesian solution where the CIs are still sensible, albeit wide. [2]