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Synthetic likelihood method for reaction network inference
Author(s) -
Linder Daniel F.,
Rempała Grzegorz A.
Publication year - 2020
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.6631
Subject(s) - markov chain monte carlo , inference , resampling , likelihood function , bayesian probability , markov chain , computer science , mathematics , bayesian inference , particle filter , marginal likelihood , data mining , algorithm , statistics , artificial intelligence , estimation theory , kalman filter
We propose a novel Markov chain Monte‐Carlo (MCMC) method for reverse engineering the topological structure of stochastic reaction networks, a notoriously challenging problem that is relevant in many modern areas of research, like discovering gene regulatory networks or analyzing epidemic spread. The method relies on projecting the original time series trajectories, from the stochastic data generating process, onto information rich summary statistics and constructing the appropriate synthetic likelihood function to estimate reaction rates. The resulting estimates are consistent in the large volume limit and are obtained without employing complicated tuning strategies and expensive resampling as typically used by likelihood‐free MCMC and approximate Bayesian methods. To illustrate the method, we apply it in two real data examples: the molecular pathway analysis with RNA‐seq and the famous incidence data from 1665 plague outbreak at Eyam, England.