Below you will find example sentences with "sample space". The examples show how this phrase is used in natural context and which words often surround it.

Sample Space in a sentence

Corpus data

  • Displayed example sentences: 20
  • Discovered as a combination around: sample
  • Corpus frequency in the collocation scan: 12
  • Phrase length: 2 words
  • Average sentence length: 23 words

Sentence profile

  • Phrase position: 5 start, 13 middle, 2 end
  • Sentence types: 20 statements, 0 questions, 0 exclamations

Corpus analysis

  • The phrase "sample space" has 2 words and usually appears in the middle in these examples. The average sentence has 23 words and is mostly made up of statements.
  • Around this phrase, patterns and context words such as called the sample space of the, to a sample space composed of, event, probability and set stand out.
  • In the phrase index, this combination connects with space station, space shuttle, space quest, sample size and sample sizes, linking the page to nearby combinations.

Example types with sample space

This selection groups the examples by length and sentence type, making usage of the full phrase easier to scan:

Continuous probability theory deals with events that occur in a continuous sample space. (13 words)

The set of all outcomes is called the sample space of the experiment. (13 words)

The collection of all possible results is called the sample space of the experiment. (14 words)

In examples such as these, the sample space (the set of all possible persons) is often suppressed, since it is mathematically hard to describe, and the possible values of the random variables are then treated as a sample space. (39 words)

An event, however, is any subset of the sample space, including any singleton set (an elementary event ), the empty set (an impossible event, with probability zero) and the sample space itself (a certain event, with probability one). (37 words)

Events in probability spaces Defining all subsets of the sample space as events works well when there are only finitely many outcomes, but gives rise to problems when the sample space is infinite. (33 words)

Example sentences (20)

Typically, when the sample space is finite, any subset of the sample space is an event (i.e. all elements of the power set of the sample space are defined as events).

An event, however, is any subset of the sample space, including any singleton set (an elementary event ), the empty set (an impossible event, with probability zero) and the sample space itself (a certain event, with probability one).

Events in probability spaces Defining all subsets of the sample space as events works well when there are only finitely many outcomes, but gives rise to problems when the sample space is infinite.

In examples such as these, the sample space (the set of all possible persons) is often suppressed, since it is mathematically hard to describe, and the possible values of the random variables are then treated as a sample space.

Under this definition only measurable subsets of the sample space, constituting a -algebra over the sample space itself, are considered events.

Multiple sample spaces For many experiments, there may be more than one plausible sample space available, depending on what result is of interest to the experimenter.

So, when defining a probability space it is possible, and often necessary, to exclude certain subsets of the sample space from being events (see Events in probability spaces, below).

The power set of the sample space (or equivalently, the event space) is formed by considering all different collections of possible results.

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That could mean far more of us get the chance to sample space travel, at least briefly.

An event is defined as a particular subset of the sample space to be considered.

Continuous probability theory deals with events that occur in a continuous sample space.

Equally likely outcomes Flipping a coin leads to a sample space composed of two outcomes that are almost equally likely.

Flipping a brass tack leads to a sample space composed of two outcomes that are not equally likely.

Here, an "event" is a set of zero or more outcomes, i.e., a subset of the sample space.

If this sum is equal to 1 then all other points can safely be excluded from the sample space, returning us to the discrete case.

In a discrete probability distribution whose sample space is finite, each elementary event is assigned a particular probability.

Second, the probability of the sample space must be equal to 1 (which accounts for the fact that, given an execution of the model, some outcome must occur).

The collection of all possible results is called the sample space of the experiment.

The set of all outcomes is called the sample space of the experiment.

This algorithm proceeds by randomly attempting to move about the sample space, sometimes accepting the moves and sometimes remaining in place.

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