System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Inexact Uniformization Method for Computing Transient Distributions of Markov Chains
SIAM Journal on Scientific Computing
Bayesian inference for a discretely observed stochastic kinetic model
Statistics and Computing
Sliding Window Abstraction for Infinite Markov Chains
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Hybrid numerical solution of the chemical master equation
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
Synthesis for PCTL in parametric Markov decision processes
NFM'11 Proceedings of the Third international conference on NASA Formal methods
Parameter estimation for stochastic hybrid models of biochemical reaction networks
Proceedings of the 15th ACM international conference on Hybrid Systems: Computation and Control
Probabilistic model checking of the PDGF signaling pathway
Transactions on Computational Systems Biology XIV
Exploring parameter space of stochastic biochemical systems using quantitative model checking
CAV'13 Proceedings of the 25th international conference on Computer Aided Verification
Polynomial-Time verification of PCTL properties of MDPs with convex uncertainties
CAV'13 Proceedings of the 25th international conference on Computer Aided Verification
On-the-fly verification and optimization of DTA-properties for large Markov chains
Formal Methods in System Design
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We propose a numerical technique for parameter inference in Markov models of biological processes. Based on time-series data of a process we estimate the kinetic rate constants by maximizing the likelihood of the data. The computation of the likelihood relies on a dynamic abstraction of the discrete state space of the Markov model which successfully mitigates the problem of state space largeness. We compare two variants of our method to state-of-the-art, recently published methods and demonstrate their usefulness and efficiency on several case studies from systems biology.