title: Poissonian steady states: from stationary densities to stationary intensities.
abstract: Markov dynamics are the most elemental and omnipresent form of stochastic dynamics in the sciences, with applications ranging from physics to chemistry, from biology to evolution, and from economics to finance. Markov dynamics can be either stationary or nonstationary. Stationary Markov dynamics represent statistical steady states and are quantified by stationary densities. In this paper, we generalize the notion of steady state to the case of general Markov dynamics. Considering an ensemble of independent motions governed by common Markov dynamics, we establish that the entire ensemble attains Poissonian steady states which are quantified by stationary Poissonian intensities and which hold valid also in the case of nonstationary Markov dynamics. The methodology is applied to a host of Markov dynamics, including Brownian motion, birth-death processes, random walks, geometric random walks, renewal processes, growth-collapse dynamics, decay-surge dynamics, Ito diffusions, and Langevin dynamics.
authors.affiliation: Holon Institute of Technology, P.O. Box 305, Holon 58102, Israel. eliazar@post.tau.ac.il
authors.forename: Iddo
authors.initials: I
authors.lastname: Eliazar
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D001703:Biophysics / Q000379:methods*; D004058:Diffusion; D019143:Evolution, Molecular; D005583:Fourier Analysis; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D009038:Motion; D017711:Nonlinear Dynamics; D010825:Physics / Q000379:methods; D016012:Poisson Distribution; D013269:Stochastic Processes