# sample space

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## sample space

[′sam·pəl ‚spās]
(statistics)
A concept in probability theory which considers all possible outcomes of an experiment, game, and so on, as points in a space.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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To map the sample space to a high or even infinite dimensional feature space by means of a nonlinear mapping plane, SVM may be a good method.
The sample space or universe (U) is defined and delimited as that made up of all of the possible ways in which element [E.sub.1] could fail during the life-cycle of the range of machines available in the country.
It make us know that, even in a small sample space, the class-specific samples will show unique features under some constraints.
Data distribution in the entire sample space will undergo continuous changes over time.
SVM is peacekeeping linear rise of, through nonlinear mapping; the sample space is mapped to a higher dimensional space within, the samples in high dimensional space correctly classified, so that you can through linear learning machine method to solve the problem of nonlinear classification in the sample space.
These include notions of sample space, possible outcomes, combinations, the probability of an event occurring and the assigning of a numerical probability measure when comparing different outcomes (Neil, 2010; Barnes, 1998).
Definition 1.1 The set of all possible outcomes of an experiment is called the sample space of that experiment and is denoted by S.
Patrangenaru and Ellingson introduce a new way of analyzing object data that primarily takes into account the geometry of the spaces of objects measured on the sample space. In the first section, they set out the three pillars of object data analysis: examples of object data, non-parametric multivariate statistics, and the geometry and topology of manifolds.
Pepinsky then claims that by doing so, it "preserves the substantive hypothesis about the predictive effects of ethnicity on BN votes, violates no assumptions about coefficient interpretability due to compositional data problems, and can be extended in a straightforward manner to interaction models." Pepinsky's claims are true--only if the sample space is in the real Euclidean space, which in this case, it is not.
Same procedure has been done until entire sample space has been exhausted.
The first step in determining the true odds that Mlodinow has HIV is to define the sample space. Mlodinow notes that we could include everyone who has ever taken an HIV test, but a more accurate result will come by employing a bit of additional relevant information.

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