View source for Random field, generalized

''generalized stochastic process''

A [[Random function|random function]] on a smooth manifold <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r0773101.png" />, the typical realizations of which are generalized functions defined on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r0773102.png" />. More precisely, let <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r0773103.png" /> be a <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r0773104.png" /> manifold (a smooth manifold) and let <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r0773105.png" /> be the space of infinitely-differentiable functions of compact support defined on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r0773106.png" />, with the usual topology of uniform convergence of sequence of functions of uniformly compact support and all their derivatives. Then a generalized random field can be defined on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r0773107.png" /> in terms of a given continuous linear mapping

<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r0773108.png" /></td> </tr></table>

from <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r0773109.png" /> into the space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731010.png" /> of random variables defined on some probability space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731011.png" /> with a specified <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731012.png" />-algebra <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731013.png" /> of subsets and a probability measure <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731014.png" /> defined on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731015.png" />; <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731016.png" /> is equipped with the topology of [[Convergence in measure|convergence in measure]] [[#References|[7]]]. In the case when the probability space is the space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731017.png" /> of generalized functions on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731018.png" /> with <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731019.png" />-algebra <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731020.png" /> generated by the cylindrical sets in <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731021.png" />, (cf. [[Generalized functions, space of|Generalized functions, space of]]; [[Cylinder set|Cylinder set]]) and the mapping is given by

<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731022.png" /></td> </tr></table>

the generalized random field <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731023.png" /> is called canonical. It turns out that any generalized random field on a finite-dimensional manifold <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731024.png" /> is probabilistically isomorphic to a certain (unique) canonical random field on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731025.png" /> (see [[#References|[2]]]).

This definition admits a number of natural modifications; for example, one can consider vector-valued generalized random fields or, instead of the space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731026.png" />, use some more extensive space of test functions on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731027.png" /> in the definition (such as, in the case <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731028.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731029.png" /> the space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731030.png" /> of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731031.png" /> differentiable functions which, together with all derivatives, decrease faster than any negative power <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731032.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731033.png" /> <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731034.png" />).

The concept of a generalized random field includes the classical random fields and processes whose realizations are ordinary functions. This concept arose in the mid-1950s, when it became apparent that many natural stochastic constructs cannot be sufficiently simply expressed in terms of classical random fields, but have in the language of generalized random fields a simple and elegant description. For example, any positive-definite bilinear form on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731035.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731036.png" />

<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731037.png" /></td> </tr></table>

<img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731038.png" />, where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731039.png" /> is a positive-definite symmetric generalized function of two variables, determines a unique Gaussian generalized random field <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731040.png" /> on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731041.png" /> (with zero average) such that the covariance of this field is

<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731042.png" /></td> </tr></table>

where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731043.png" /> is the probability measure on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731044.png" /> corresponding to this field. This generalized random field turns out to be classical only if the function <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731045.png" /> is sufficiently good (for example, continuous and bounded). Other examples are: generalized random fields on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731046.png" /> (see [[#References|[6]]]), among which there are no classical fields.

Interest in the study of generalized random fields (and especially of Markov fields) has grown recently due to the connection, discovered in the early 1970s, between the problem of constructing physical quantum fields and Markov generalized random fields on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731047.png" /> when <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731048.png" /> (see [[#References|[5]]]).

====References====
<table><TR><TD valign="top">[1]</TD> <TD valign="top">  I.M. Gel'fand,   G.E. Shilov,   "Generalized functions. Spaces of fundamental and generalized functions" , Acad. Press  (1968)  (Translated from Russian)</TD></TR><TR><TD valign="top">[2]</TD> <TD valign="top">  I.M. Gel'fand,   N.Ya. Vilenkin,   "Generalized functions. Applications of harmonic analysis" , '''4''' , Acad. Press  (1968)  (Translated from Russian)</TD></TR><TR><TD valign="top">[3]</TD> <TD valign="top">  I.M. Gel'fand,   "Generalized random processes"  ''Dokl. Akad. Nauk SSSR'' , '''100''' :  5  (1955)  pp. 853–856  (In Russian)</TD></TR><TR><TD valign="top">[4]</TD> <TD valign="top">  H. Itô,   "Stationary random distributions"  ''Mem. Coll. Sci. Univ. Kyoto Ser. A.'' , '''28''' :  3  (1954)  pp. 209–223</TD></TR><TR><TD valign="top">[5]</TD> <TD valign="top">  B. Simon,   "The <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/r/r077/r077310/r07731049.png" /> Euclidean (quantum) field theory" , Princeton Univ. Press  (1974)</TD></TR><TR><TD valign="top">[6]</TD> <TD valign="top">  R.L. Dobrushin,   "Automodel generalized random fields and their renorm group"  R.L. Dobrushin (ed.)  Ya.G. Sinai (ed.) , ''Multicomponent Random Sets'' , M. Dekker  (1980)  pp. 153–198  (Translated from Russian)</TD></TR><TR><TD valign="top">[7]</TD> <TD valign="top">  R.L. Dobrushin,   R.A. Minlos,   "Polynomials in linear random functions"  ''Russian Math. Surveys'' , '''32''' :  2  (1971)  pp. 71–127  ''Uspekhi Mat. Nauk'' , '''32''' :  2  (1977)  pp. 67–122</TD></TR></table>



====Comments====
Cf. also [[Random field|Random field]].

====References====
<table><TR><TD valign="top">[a1]</TD> <TD valign="top">  A.M. Yaglom,   "Correlation theory of stationary and related random functions" , '''1–2''' , Springer  (1987)  (Translated from Russian)</TD></TR><TR><TD valign="top">[a2]</TD> <TD valign="top">  S. Albeverio,   R. Høegh-Krohn,   B. Zegarlinski,   "Uniqueness and global Markov property for Euclidean fields: the case of general polynomial interactions"  ''Commun. Math. Phys.'' , '''123'''  (1989)  pp. 377–424</TD></TR><TR><TD valign="top">[a3]</TD> <TD valign="top">  Yu.A. Rozanov,   "Markov random fields" , Springer  (1982)  (Translated from Russian)</TD></TR></table>