State and parameter estimation from exact partial state observation in stochastic reaction networks

We consider chemical reaction networks modeled by a discrete state and continuous in time Markov process for the vector copy number of species provide novel particle filter method parameter estimation based on exact observation some time. The conditional probability distribution unobserved states is shown to satisfy system differential equations with jumps. simulating that proxy along weight. resulting weighted Monte Carlo simulation then used compute species. also show how our algorithm can be adapted Bayesian parameters past value observations up future