Fluctuation Statistics in Networks: a Stochastic Path Integral Approach
  We investigate the statistics of fluctuations in a classical stochastic network of nodes joined by connectors. The nodes carry generalized charge that may be randomly transferred from one node to another. Our goal is to find the time evolution of the probability distribution of charges in the network. The building blocks of our theoretical approach are (1) known probability distributions for the connector currents, (2) physical constraints such as local charge conservation, and (3) a time-scale separation between the slow charge dynamics of the nodes and the fast current fluctuations of the connectors. We derive a stochastic path integral representation of the evolution operator for the slow charges. Once the probability distributions on the discrete network have been studied, the continuum limit is taken to obtain a statistical field theory. We find a correspondence between the diffusive field theory and a Langevin equation with Gaussian noise sources, leading nevertheless to non-trivial fluctuation statistics. To complete our theory, we demonstrate that the cascade diagrammatics, recently introduced by Nagaev, naturally follows from the stochastic path integral. We extend the diagrammatics to calculate current correlation functions for an arbitrary network. One primary application of this formalism is that of full counting statistics (FCS). We stress however, that the formalism is suitable for general classical stochastic problems as an alternative to the traditional master equation or Doi-Peliti technique. The formalism is illustrated with several examples: both instantaneous and time averaged charge fluctuation statistics in a mesoscopic chaotic cavity, as well as the FCS and new results for a generalized diffusive wire. 
Andrew N. Jordan, Eugene V. Sukhorukov, and Sebastian Pilgram