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In the present work the master equation is applied to the phenomena of coalescence and dispersion.
The master equation is a stochastic population balance, applicable to most systems composed of discrete entities.
An approximate solution of the master equation is obtained in the limit of a small learning rate.
The chemical master equation is solved by a hybrid method coupling a macroscopic, deterministic description with a mesoscopic, stochastic model.
One limitation of both versions of the neural master equation is that they neglect the dynamics of synaptic currents.
The chemical master equation is often used to simulate the Markov process and enable computational studies of GRN.
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Monte Carlo simulation of chemically reacting systems based on the master equation was used to describe the stochastic time evolution of the microemulsion polymerization system.
Analytical solutions to the master equation are hard to come by though because they require the enumeration of all possible states and the determination of the transition probabilities between any two states.
In our primary reference for this model [5], actually a bounded state space version of the master equation was considered where the activation rate for the event θ k → θ k + 1 is l ( k ) f ¯ k ( θ, t ) I [ θ k < l ( k ) ], (3.6).
Both analytic and numerical solutions of this chemical master equation are difficult to obtain for most biologically relevant systems.
Ensuring that subvolume size was larger than the accepted lower bound, and significantly larger than the "critical value" discussed in [ 38], the deviation of the stationary distribution of the reactant from the analytical solution to the master equation was small.
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