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For the general partially-observed framework of Markov processes with marked point process observations proposed in , we develop the corresponding Bayesian model selection via filtering equations to quantify model uncertainty. To achieve this, we first derive the unnormalized filtering equation and the system of ratio filtering equations to, respectively, characterize the evolution of the marginal likelihood and the corresponding Bayes factors. Then, we prove a powerful weak convergencedoi:10.1137/16m1094774 fatcat:exynnrajlnaahka4kyeybvjt54