You get a bonus - 1 coin for daily activity. Now you have 1 coin

Elementary event space

Lecture



Elementary event space - set   Elementary event space all the different outcomes of a random experiment.

Element of this set   Elementary event space called an elementary event or outcome . The space of elementary events is called discrete if the number of its elements is finite or countable. Any space of elementary events that is not discrete is called nondiscrete , and if the observed results (cannot be pronounced random events) are points of one or another numerical arithmetic or coordinate space, then the space is called continuous ( continuum ). Elementary event space   Elementary event space together with the algebra of events   Elementary event space and probability   Elementary event space forms the top three   Elementary event space called probabilistic space.

Elementary event [edit]

In probability theory, elementary events or atomic events are the outcomes of a random experiment, of which exactly one occurs in the experiment. The set of all elementary events is usually denoted.   Elementary event space .

Every subset of a set   Elementary event space Elementary events are called random events. It is said that a random event occurred as a result of the experiment.   Elementary event space if the (elementary) outcome of the experiment is an element   Elementary event space .

In the definition of a probability space on a set of random events, a sigma-additive finite measure, called probability, is introduced.

Elementary events can have probabilities that are strictly positive, zero, indefinite, or any combination of these options. For example, any discrete probability distribution is determined by the probabilities of what may be called elementary events. In contrast, all elementary events have zero probability for continuous distribution. Mixed distributions, being neither continuous nor discrete, can contain atoms that can be thought of as elementary (i.e. event-atoms ) events with a non-zero probability. In the theory of measure in the definition of a probability space, the probability of an arbitrary elementary event could not be determined until the mathematicians saw the difference between the outcome space S and events that are of interest, and which are defined as elements of the σ-algebra of events from S.

Formally speaking, an elementary event is a subset of the outcome space of a random experiment, which consists of only one element; that is, an elementary event is still a multitude, but not the element itself. However, elementary events are usually recorded as elements, and not as sets for the purpose of simplification, when this cannot cause misunderstandings.

Examples

Examples of experiment outcome spaces,   Elementary event space and elementary events:

  • If objects are countable, and the outcome space   Elementary event space (natural numbers), elementary events are any sets   Elementary event space where   Elementary event space .
  • If the coin is thrown twice,   Elementary event space ,   Elementary event space for eagle as well   Elementary event space for tails, then the elementary events:   Elementary event space ,   Elementary event space ,   Elementary event space and   Elementary event space .
  • If a   Elementary event space - these are normally distributed random variables,   Elementary event space , real numbers, then elementary events - any sets   Elementary event space where   Elementary event space . This example shows that the continuous probability distribution is not determined by the probabilities of atom events, since here the probabilities of all elementary events are zero.
created: 2014-12-31
updated: 2021-03-13
132566



Rating 9 of 10. count vote: 2
Are you satisfied?:



Comments


To leave a comment
If you have any suggestion, idea, thanks or comment, feel free to write. We really value feedback and are glad to hear your opinion.
To reply

Probability theory. Mathematical Statistics and Stochastic Analysis

Terms: Probability theory. Mathematical Statistics and Stochastic Analysis