Efficient variants of the minimal diffusion formulation of Markov chain ensembles

Strong diffusion formulation of Markov chain ensembles and its optimal weaker reductions

Two self-contained diffusion formulations, in the form of coupled stochastic differential equations, are developed for the temporal evolution of state densities over an ensemble of Markov chains evolving independently under a common transition rate matrix. Our first formulation derives from Kurtz's strong approximation theorem of density-dependent Markov jump processes [Stoch.

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Their Appl. 6, 223 (1978)STOPB70304-414910.1016/0304-4149(78)90020-0] and, therefore, strongly converges with an error bound of the order of lnN/N for ensemble size N. The second formulation eliminates some fluctuation variables, and correspondingly some noise terms, within the governing equations of the strong formulation, with the objective of achieving a simpler analytic formulation and a faster computation algorithm when the transition rates are constant or slowly varying. There, the reduction of the structural complexity is optimal in the sense that the elimination of any given set of variables takes place with the lowest attainable increase in the error bound. The resultant formulations are supported by numerical simulations.