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High-intensity interval training (HIIT) is a training protocol alternating short periods of intense or explosive anaerobic exercise with brief recovery periods until the point of exhaustion. [1] HIIT involves exercises performed in repeated quick bursts at maximum or near maximal effort with periods of rest or low activity between bouts.
A comprehensive paper on interval algebra in numerical analysis was published by Teruo Sunaga (1958). [11] The birth of modern interval arithmetic was marked by the appearance of the book Interval Analysis by Ramon E. Moore in 1966. [12] [13] He had the idea in spring 1958, and a year later he published an article about computer interval ...
Interval training is a type of training exercise that involves a series of high-intensity workouts interspersed with rest or break periods. The high-intensity periods are typically at or close to anaerobic exercise, while the recovery periods involve activity of lower intensity. [1]
The principle is to evaluate f(x) using interval arithmetic (this is the forward step). The resulting interval is intersected with [y]. A backward evaluation of f(x) is then performed in order to contract the intervals for the x i (this is the backward step). We now illustrate the principle on a simple example.
Boxes present the advantage of being very easily manipulated by computers, as they form the heart of interval analysis. Many interval algorithms naturally provide solutions that are regular subpavings. [1] In computation, a well-known application of subpaving in R² is the Quadtree data structure.
Neyman construction, named after Jerzy Spława-Neyman, is a frequentist method to construct an interval at a confidence level, such that if we repeat the experiment many times the interval will contain the true value of some parameter a fraction of the time.
In statistics, interval estimation is the use of sample data to estimate an interval of possible values of a parameter of interest. This is in contrast to point estimation, which gives a single value. [1] The most prevalent forms of interval estimation are confidence intervals (a frequentist method) and credible intervals (a Bayesian method). [2]
Given a sample from a normal distribution, whose parameters are unknown, it is possible to give prediction intervals in the frequentist sense, i.e., an interval [a, b] based on statistics of the sample such that on repeated experiments, X n+1 falls in the interval the desired percentage of the time; one may call these "predictive confidence intervals".