Convergence rates for Markov chains
SIAM Review
Hybrid simulation of cellular behavior
Bioinformatics
Research Article: Hybrid stochastic simulations of intracellular reaction-diffusion systems
Computational Biology and Chemistry
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
International Journal of High Performance Computing Applications
Sensitivity analysis of stochastic models of bistable biochemical reactions
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques
Efficient Formulations for Exact Stochastic Simulation of Chemical Systems
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Exploring the performance of spatial stochastic simulation algorithms
Journal of Computational Physics
Streamlined formulation of adaptive explicit-implicit tau-leaping with automatic tau selection
Winter Simulation Conference
Modelling and analysis of the NF-κB pathway in bio-PEPA
Transactions on Computational Systems Biology XII
Don't just go with the flow: cautionary tales of fluid flow approximation
EPEW'12 Proceedings of the 9th European conference on Computer Performance Engineering
Don't just go with the flow: cautionary tales of fluid flow approximation
EPEW'12 Proceedings of the 9th European conference on Computer Performance Engineering
Markov Chain Simulation with Fewer Random Samples
Electronic Notes in Theoretical Computer Science (ENTCS)
A First-Passage Kinetic Monte Carlo method for reaction-drift-diffusion processes
Journal of Computational Physics
Hi-index | 31.46 |
This paper introduces the concept of distribution distance for the measurement of errors in exact and approximate methods for stochastic simulation of chemically reacting systems. Two types of distance are discussed: the Kolmogorov distance and the histogram distance. The self-distance, an important property of Monte-Carlo methods that quantifies the accuracy limitation at a given resolution for a given number of realizations, is defined and studied. Estimation formulas are established for the histogram and the Kolmogorov self-distance. These formulas do not depend on the distribution of the samples, and thus show a property of the Monte-Carlo method itself. Numerical results demonstrate that the formulas are very accurate. Application of these results to two problems of current interest in the simulation of biochemical systems is discussed.