By Daniel Evan Jones

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**Extra info for Examples in physics**

**Example text**

In the theory of stochastic processes as a wider class of functions has to be frequently considered than the ordinary time-functions. These functions are then regarded as operators that include time derivatives and integrals of the relevant random variables and must be determined in accordance with the rules of stochastic calculus. 1HO =0 . s. derivative at a point E T is then: t 82 8t8sE{x(t)x(s)} exists at (t,s) = (t,t). : ml,2 ... ,n(tl, t2'" tn) = E{Xl, X2 ... 159(b)) can be generalized to: 8 )kl( -8 8 )k2 ...

Conventionally, one may denote by F 00 the "smallest a-algebra on n" containing all events in F t for all t ~ 0 and assume without loss of generality, that the "ambient" a-algebra F = Foo (see also [47]). In this sense, whenever the triple [n, F, P] is used as a "filtered probability space", it is understood that the abstract dynamical system is characterized by a probability space n or X and that IF = {Ft , t ~ O} is a filtration of [n, F] with F = Foo. Associated with filtered probabilty spaces is the notion of a "stopping time r".

The function Px( .. 65b) is then the joint density function of the random variables Xl, X2 ••• Xn" . It is apparent, that one can also consider a fully "conditional probability space", which is generated by the a-finite measure P on the a-algebra F and define the corresponding "conditional distribution and density functions" . 67) It follows that for an n-dimensional "random vector x = {Xl, X2 use of the chain rule of conditional probabilities one obtains: ••• x n }" by the n-l p{6,6··· en) = p(en) II p{eilei+l ...

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