Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
Stochasticity is a key characteristic of intracellular processes such as gene regulation and
chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is …
chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is …
Multilevel monte carlo methods
MB Giles - Acta numerica, 2015 - cambridge.org
Monte Carlo methods are a very general and useful approach for the estimation of
expectations arising from stochastic simulation. However, they can be computationally …
expectations arising from stochastic simulation. However, they can be computationally …
Goal-oriented adaptive finite element multilevel Monte Carlo with convergence rates
In this study, we present an adaptive multilevel Monte Carlo (AMLMC) algorithm for
approximating deterministic, real-valued, bounded linear functionals that depend on the …
approximating deterministic, real-valued, bounded linear functionals that depend on the …
Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks
Abstract The multilevel Monte Carlo (MLMC) method for continuous-time Markov chains, first
introduced by Anderson and Higham (SIAM Multiscal Model Simul 10 (1): 146–179, 2012) …
introduced by Anderson and Higham (SIAM Multiscal Model Simul 10 (1): 146–179, 2012) …
Multilevel hybrid split-step implicit tau-leap
C Ben Hammouda, A Moraes, R Tempone - Numerical Algorithms, 2017 - Springer
In biochemically reactive systems with small copy numbers of one or more reactant
molecules, the dynamics is dominated by stochastic effects. To approximate those systems …
molecules, the dynamics is dominated by stochastic effects. To approximate those systems …
Learning-based importance sampling via stochastic optimal control for stochastic reaction networks
We explore efficient estimation of statistical quantities, particularly rare event probabilities,
for stochastic reaction networks. Consequently, we propose an importance sampling (IS) …
for stochastic reaction networks. Consequently, we propose an importance sampling (IS) …
An efficient forward–reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks
In this work, we present an extension of the forward–reverse representation introduced by
Bayer and Schoenmakers (Annals of Applied Probability, 24 (5): 1994–2032, 2014) to the …
Bayer and Schoenmakers (Annals of Applied Probability, 24 (5): 1994–2032, 2014) to the …
Non-nested adaptive timesteps in multilevel Monte Carlo computations
This paper shows that it is relatively easy to incorporate adaptive timesteps into multilevel
Monte Carlo simulations without violating the telescoping sum on which multilevel Monte …
Monte Carlo simulations without violating the telescoping sum on which multilevel Monte …
[HTML][HTML] Automated importance sampling via optimal control for stochastic reaction networks: A Markovian projection–based approach
We propose a novel alternative approach to our previous work (Ben Hammouda et al., 2023)
to improve the efficiency of Monte Carlo (MC) estimators for rare event probabilities for …
to improve the efficiency of Monte Carlo (MC) estimators for rare event probabilities for …
Multiscale simulation of stochastic reaction-diffusion networks
The most commonly employed spatial stochastic simulation methods for biochemical
systems in molecular systems biology are reviewed from a multiscale perspective. Three …
systems in molecular systems biology are reviewed from a multiscale perspective. Three …