Borel right process

In the mathematical theory of probability, a Borel right process, named after Émile Borel, is a particular kind of continuous-time random process.

Let E be a locally compact, separable, metric space.

We denote by \mathcal E the Borel subsets of E.

Let \Omega be the space of right continuous maps from [0,\infty) to E that have left limits in E,

and for each t \in [0,\infty), denote by X_t the coordinate map at t; for

each \omega \in \Omega , X_t(\omega) \in E is the value of \omega at t.

We denote the universal completion of \mathcal E by \mathcal E^*.

For each t\in[0,\infty), let

:

\mathcal F_t = \sigma\left\{ X_s^{-1}(B) : s\in[0,t], B \in \mathcal E\right\},

:

\mathcal F_t^* = \sigma\left\{ X_s^{-1}(B) : s\in[0,t], B \in \mathcal E^*\right\},

and then, let

:

\mathcal F_\infty = \sigma\left\{ X_s^{-1}(B) : s\in[0,\infty), B \in \mathcal E\right\},

:

\mathcal F_\infty^* = \sigma\left\{ X_s^{-1}(B) : s\in[0,\infty), B \in \mathcal E^*\right\}.

For each Borel measurable function f on E, define, for each x \in E,

:

U^\alpha f(x) = \mathbf E^x\left[ \int_0^\infty e^{-\alpha t} f(X_t)\, dt \right].

Since P_tf(x) = \mathbf E^x\left[f(X_t)\right] and the mapping given by t \rightarrow X_t is right continuous, we see that

for any uniformly continuous function f, we have the mapping given by t \rightarrow P_tf(x) is right continuous.

Therefore, together with the monotone class theorem, for any universally measurable function f, the mapping given by (t,x) \rightarrow P_tf(x), is jointly measurable, that is, \mathcal B([0,\infty))\otimes \mathcal E^* measurable, and subsequently, the mapping is also \left(\mathcal B([0,\infty))\otimes \mathcal E^*\right)^{\lambda\otimes \mu}-measurable for all finite measures \lambda on \mathcal B([0,\infty)) and \mu on \mathcal E^*.

Here,

\left(\mathcal B([0,\infty))\otimes \mathcal E^*\right)^{\lambda\otimes \mu} is the completion of

\mathcal B([0,\infty))\otimes \mathcal E^* with respect

to the product measure \lambda \otimes \mu.

Thus, for any bounded universally measurable function f on E,

the mapping t\rightarrow P_tf(x) is Lebeague measurable, and hence,

for each \alpha \in [0,\infty) , one can define

:

U^\alpha f(x) = \int_0^\infty e^{-\alpha t}P_tf(x) dt.

There is enough joint measurability to check that \{U^\alpha : \alpha \in (0,\infty)

\} is a Markov resolvent on (E,\mathcal E^*),

which uniquely associated with the Markovian semigroup \{ P_t : t \in [0,\infty) \}.

Consequently, one may apply Fubini's theorem to see that

:

U^\alpha f(x) = \mathbf E^x\left[ \int_0^\infty e^{-\alpha t} f(X_t) dt \right].

The following are the defining properties of Borel right processes:{{harvnb|Sharpe|1988|loc=Sect. 20}}

  • Hypothesis Droite 1:

:For each probability measure \mu on (E, \mathcal E), there exists a probability measure \mathbf P^\mu on (\Omega, \mathcal F^*) such that (X_t, \mathcal F_t^*, P^\mu) is a Markov process with initial measure \mu and transition semigroup \{ P_t : t \in [0,\infty) \}.

  • Hypothesis Droite 2:

:Let f be \alpha-excessive for the resolvent on (E, \mathcal E^*). Then, for each probability measure \mu on (E,\mathcal E), a mapping given by t \rightarrow f(X_t) is P^\mu almost surely right continuous on [0,\infty).

Notes

References

  • {{citation|last=Sharpe|first=Michael|title=General Theory of Markov Processes|year=1988|isbn=0126390606}}

{{DEFAULTSORT:Borel Right Process}}

Category:Stochastic processes