Continuous Time Markov Chain Models for Chemical Reaction Networks

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A Markov Jump Process for More Efficient Hamiltonian Monte Carlo (2015, methodology): “Markov jump processes have been used to model physical systems, such as chemical reactions, which are well described by stochastic transitions between discrete states [4].”
Causal interpretation of stochastic differential equations (2013, methodology): “r) system of ODEs for the concentrations is d dt Xt Yt = Sλ(Xt,Yt) = 0 a +B Xt Yt (2.3) with B = −b11 b12 −b12 −b22 (2.4) . A system of SDEs approximating the Markov jump process, see [2], is given by Xt Yt = X0 Y0 +at + Z t 0 B Xs Ys ds + Z t 0 (2.5) Σ(Xs,Ys) dWs where Ws denotes a fo”