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Low Variance Couplings for Stochastic Models of Intracellular Processes with Time-Dependent Rate Functions
David F Anderson1, Chaojie Yuan2
1Department of Mathematics, University of Wisconsin, Madison, USA. anderson@math.wisc.edu.
Bulletin of Mathematical Biology
|April 20, 2018
Summary
New coupling strategies for biochemical processes reduce simulation variance. Stacked coupling offers low variance for sensitivity analysis and faster estimations using multilevel Monte Carlo methods.
Area of Science:
- Biochemical processes modeling
- Computational stochastic methods
Background:
- Stochastic models are crucial for biochemical processes.
- Time-dependent parameters complicate simulations.
- Efficient numerical methods are needed.
Purpose of the Study:
- Introduce novel coupling strategies for stochastic biochemical processes.
- Present the 'stacked coupling' method.
- Demonstrate its effectiveness in reducing simulation variance.
Main Methods:
- Development of coupling strategies for time-dependent parameters.
- Introduction and application of stacked coupling.
- Numerical computation of parametric sensitivities.
- Estimation of expectations using multilevel Monte Carlo.
Main Results:
- Stacked coupling significantly reduces variance in generated paths.
- Demonstrated utility in numerical computation of parametric sensitivities.
- Enabled fast estimation of expectations via multilevel Monte Carlo methods.
Conclusions:
- Stacked coupling is an effective strategy for stochastic biochemical models.
- The method enhances efficiency in sensitivity analysis and expectation estimation.
- Provides essential estimators for these computations.
Keywords:
Gillespie algorithmNon-homogeneous Markov chainsStochastic chemical kineticsStochastic simulationVariance reductionMore Related Videos
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