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A sample space is usually denoted using set notation, and the possible ordered outcomes, or sample points, [5] are listed as elements in the set. It is common to refer to a sample space by the labels S, Ω, or U (for "universal set"). The elements of a sample space may be numbers, words, letters, or symbols.
[1] [2] The 2005 NASA Authorization Act designated the American segment of the International Space Station as a national laboratory with the goal of increasing the use of the ISS by other federal agencies and the private sector. [3] Research on the ISS improves knowledge about the effects of long-term space exposure on the human body.
It was the first NASA sample-return mission to return material since the Apollo program, and the first to return material from beyond the orbit of the Moon. [4] [5] Genesis was launched on August 8, 2001, and the sample return capsule crash-landed in Utah on September 8, 2004, after a design flaw prevented the deployment of its drogue parachute ...
This leads to different choices of sample space. The σ-algebra is a collection of all the events we would like to consider. This collection may or may not include each of the elementary events. Here, an "event" is a set of zero or more outcomes; that is, a subset of the sample space. An event is considered to have "happened" during an ...
In Latin hypercube sampling one must first decide how many sample points to use and for each sample point remember in which row and column the sample point was taken. Such configuration is similar to having N rooks on a chess board without threatening each other. In orthogonal sampling, the sample space is partitioned into equally probable ...
Stardust was a 385-kilogram robotic space probe launched by NASA on 7 February 1999. Its primary mission was to collect dust samples from the coma of comet Wild 2, as well as samples of cosmic dust, and return them to Earth for analysis.
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The main useful property of small-bias sample spaces is that they need far fewer truly random bits than the uniform distribution to fool parities. Efficient constructions of small-bias sample spaces have found many applications in computer science, some of which are derandomization, error-correcting codes, and probabilistically checkable proofs.