# Difference between revisions of "Probability space"

(60A05) |
m |
||

(2 intermediate revisions by one other user not shown) | |||

Line 1: | Line 1: | ||

− | + | ||

{{MSC|60A05}} | {{MSC|60A05}} | ||

+ | {{TEX|done}} | ||

$ \newcommand{\R}{\mathbb R} | $ \newcommand{\R}{\mathbb R} | ||

Line 7: | Line 8: | ||

\newcommand{\A}{\mathcal A} | \newcommand{\A}{\mathcal A} | ||

\newcommand{\P}{\mathbf P} $ | \newcommand{\P}{\mathbf P} $ | ||

− | A triple | + | A ''probability space'' (or also ''probability field'') is a triple |

$(\Om,\A,\P)$ consisting of a non-empty set $\Om$, a class $\A$ of | $(\Om,\A,\P)$ consisting of a non-empty set $\Om$, a class $\A$ of | ||

subsets of $\Om$ which is a σ-algebra (i.e. is closed with respect to | subsets of $\Om$ which is a σ-algebra (i.e. is closed with respect to | ||

Line 14: | Line 15: | ||

[[Probability measure|probability measure]] $\P$ on $\A$. The concept | [[Probability measure|probability measure]] $\P$ on $\A$. The concept | ||

of a probability space is due to A.N. Kolmogorov | of a probability space is due to A.N. Kolmogorov | ||

− | + | {{Cite|Ko}}. The points of $\Om$ are said to be elementary | |

events, while the set $\Om$ itself is referred to as the space of | events, while the set $\Om$ itself is referred to as the space of | ||

elementary events or the sample space. The subsets of $\Om$ belonging | elementary events or the sample space. The subsets of $\Om$ belonging | ||

Line 27: | Line 28: | ||

measure on $\A$. The space $(\Om,\overline{\A},\overline{\P})$ is | measure on $\A$. The space $(\Om,\overline{\A},\overline{\P})$ is | ||

complete and is said to be the completion of $(\Om,\A,\P)$. Usually | complete and is said to be the completion of $(\Om,\A,\P)$. Usually | ||

− | one may restrict attention perfect probability spaces, i.e. spaces | + | one may restrict attention to perfect probability spaces, i.e. spaces |

such that for any real $\A$-measurable function $f$ and any set $E$ on | such that for any real $\A$-measurable function $f$ and any set $E$ on | ||

the real line for which $f^{-1}(E)\in\A$, there exists a Borel set $B$ | the real line for which $f^{-1}(E)\in\A$, there exists a Borel set $B$ | ||

Line 54: | Line 55: | ||

$$\P_{t_1,\dots,t_n}(B) = \P\{x\in R^T\;:\;t_1(x),\dots,t_n(x) \in B \}.$$ | $$\P_{t_1,\dots,t_n}(B) = \P\{x\in R^T\;:\;t_1(x),\dots,t_n(x) \in B \}.$$ | ||

====References==== | ====References==== | ||

− | + | {| | |

− | + | |- | |

− | + | |valign="top"|{{Ref|Bi}}||valign="top"| P. Billingsley, "Probability and measure", Wiley (1979) {{MR|0534323}} {{ZBL|0411.60001}} | |

− | + | |- | |

− | + | |valign="top"|{{Ref|GnKo}}||valign="top"| B.V. Gnedenko, A.N. Kolmogorov, "Limit distributions for sums of independent random variables", Addison-Wesley (1954) (Translated from Russian) {{MR|0062975}} {{ZBL|0056.36001}} | |

− | + | |- | |

− | + | |valign="top"|{{Ref|Ko}}||valign="top"| A.N. Kolmogorov, "Foundations of the theory of probability", Chelsea, reprint (1950) (Translated from Russian) {{MR|0032961}} {{ZBL|0074.12202}} | |

− | + | |- | |

− | + | |valign="top"|{{Ref|Ne}}||valign="top"| J. Neveu, "Mathematical foundations of the calculus of probabilities", Holden-Day (1965) (Translated from French) {{MR|0198505}} | |

− | + | |- | |

− | + | |} | |

− | |||

− | |||

− | |||

− | |||

− | |||

− |

## Latest revision as of 17:20, 11 May 2013

2010 Mathematics Subject Classification: *Primary:* 60A05 [MSN][ZBL]

$ \newcommand{\R}{\mathbb R}
\newcommand{\Om}{\Omega}
\newcommand{\A}{\mathcal A}
\newcommand{\P}{\mathbf P} $
A *probability space* (or also *probability field*) is a triple
$(\Om,\A,\P)$ consisting of a non-empty set $\Om$, a class $\A$ of
subsets of $\Om$ which is a σ-algebra (i.e. is closed with respect to
the set-theoretic operations executed a countable number of times)
and a
probability measure $\P$ on $\A$. The concept
of a probability space is due to A.N. Kolmogorov
[Ko]. The points of $\Om$ are said to be elementary
events, while the set $\Om$ itself is referred to as the space of
elementary events or the sample space. The subsets of $\Om$ belonging
to $\A$ are (random) events. The study of probability spaces is often
restricted to the study of complete probability spaces, i.e. spaces
which satisfy the requirement $B\in\A$, $A\subset B$, $\P(B)=0$
implies $A\in\A$. If $(\Om,\A,\P)$ is an arbitrary probability space,
the class of sets of the type $A\cup N$, where $A\in\A $ and $N\subset
M$, for some $M\in\A$ with $\P(M)=0$, forms a σ-algebra
$\overline{\A}$, while the function $\overline{\P}$ on $\overline{\A}$
defined by the formula $\overline{\P}(A\cup N)=\P(A)$ is a probability
measure on $\A$. The space $(\Om,\overline{\A},\overline{\P})$ is
complete and is said to be the completion of $(\Om,\A,\P)$. Usually
one may restrict attention to perfect probability spaces, i.e. spaces
such that for any real $\A$-measurable function $f$ and any set $E$ on
the real line for which $f^{-1}(E)\in\A$, there exists a Borel set $B$
such that $B\subset E$ and $\P(f^{-1}(E))=\P(f^{-1}(B))$. Certain
"pathological" effects (connected with the existence of conditional
probabilities, the definition of independent random variables, etc.),
which occur in the general scheme, cannot occur in perfect probability
spaces. The problem of the existence of probability spaces satisfying
some given special requirements is not trivial in many cases. One
result of this type is the fundamental Kolmogorov consistency theorem:
Let to each ordered $n$-tuple $t_1,\dots,t_n$ of elements of a set $T$
correspond a probability measure $\P_{t_1,\dots,t_n}$ on the Borel
sets of the Euclidean space $\R^n$ and let the following consistency
conditions be satisfied:

- $\def\a{\alpha}\P_{t_1,\dots,t_n}(l_{y_1,\dots,y_n}) = \P_{t_{\a_1},\dots,t_{\a_n}}(l_{y_{\a_1},\dots,y_{\a_n}})$ for all $(y_1,\dots,y_n) \in \R^n$, where $l_{y_1,\dots,y_n} = \{x = (x_1,\dots,x_n)\;:\; x_i\le y_i,\ i=1,\dots,n\}$ and $\a_1,\dots,\a_n$ is an arbitrary rearrangement of the numbers $1,\dots,n$;
- $\P_{t_1,\dots,t_n}(l_{y_1,\dots,y_{n-1},\infty}) = \P_{t_1,\dots,t_{n-1}}(l_{y_1,\dots,y_{n-1}})$.

Then there exists a probability measure $\P$ on the smallest $\sigma$-algebra $\A$ of subsets of the product $\R^T = \{x = \{x_t\}\;:\ t\in T,\;x_t\in \R^1\}$ with respect to which all the coordinate functions $t(x) = x_t$ are measurable, such that for any finite subset $t_1,\dots,t_n$ of $T$ and for any $n$-dimensional Borel set $B$ the following equation is true: $$\P_{t_1,\dots,t_n}(B) = \P\{x\in R^T\;:\;t_1(x),\dots,t_n(x) \in B \}.$$

#### References

[Bi] | P. Billingsley, "Probability and measure", Wiley (1979) MR0534323 Zbl 0411.60001 |

[GnKo] | B.V. Gnedenko, A.N. Kolmogorov, "Limit distributions for sums of independent random variables", Addison-Wesley (1954) (Translated from Russian) MR0062975 Zbl 0056.36001 |

[Ko] | A.N. Kolmogorov, "Foundations of the theory of probability", Chelsea, reprint (1950) (Translated from Russian) MR0032961 Zbl 0074.12202 |

[Ne] | J. Neveu, "Mathematical foundations of the calculus of probabilities", Holden-Day (1965) (Translated from French) MR0198505 |

**How to Cite This Entry:**

Probability space.

*Encyclopedia of Mathematics.*URL: http://encyclopediaofmath.org/index.php?title=Probability_space&oldid=20646