English

A Ruelle Operator for continuous time Markov Chains

Dynamical Systems 2013-01-22 v4 Probability

Abstract

We consider a generalization of the Ruelle theorem for the case of continuous time problems. We present a result which we believe is important for future use in problems in Mathematical Physics related to CC^*-Algebras We consider a finite state set SS and a stationary continuous time Markov Chain XtX_t, t0t\geq 0, taking values on S. We denote by Ω\Omega the set of paths ww taking values on S (the elements ww are locally constant with left and right limits and are also right continuous on tt). We consider an infinitesimal generator LL and a stationary vector p0p_0. We denote by PP the associated probability on (Ω,B\Omega, {\cal B}). This is the a priori probability. All functions ff we consider bellow are in the set L(P){\cal L}^\infty (P). From the probability PP we define a Ruelle operator Lt,t0{\cal L}^t, t\geq 0, acting on functions f:ΩRf:\Omega \to \mathbb{R} of L(P){\cal L}^\infty (P). Given V:ΩRV:\Omega \to \mathbb{R}, such that is constant in sets of the form {X0=c}\{X_0=c\}, we define a modified Ruelle operator L~Vt,t0\tilde{{\cal L}}_V^t, t\geq 0. We are able to show the existence of an eigenfunction uu and an eigen-probability νV\nu_V on Ω\Omega associated to L~Vt,t0\tilde{{\cal L}}^t_V, t\geq 0. We also show the following property for the probability νV\nu_V: for any integrable gL(P)g\in {\cal L}^\infty (P) and any real and positive tt e0t(VΘs)(.)ds[(L~Vt(g))θt]dνV=gdνV \int e^{-\int_0^t (V \circ \Theta_s)(.) ds} [ (\tilde{{\cal L}}^t_V (g)) \circ \theta_t ] d \nu_V = \int g d \nu_V This equation generalize, for the continuous time Markov Chain, a similar one for discrete time systems (and which is quite important for understanding the KMS states of certain CC^*-algebras).

Keywords

Cite

@article{arxiv.0803.2501,
  title  = {A Ruelle Operator for continuous time Markov Chains},
  author = {Alexandre Baraviera and Ruy Exel and Artur O. Lopes},
  journal= {arXiv preprint arXiv:0803.2501},
  year   = {2013}
}
R2 v1 2026-06-21T10:22:12.782Z