Monte Carlo methods are a very general and useful approach for the estimation of
expectations arising from stochastic simulation. However, they can be computationally …

We propose and analyze a novel multi-index Monte Carlo (MIMC) method for weak
approximation of stochastic models that are described in terms of differential equations …

Nowadays, mathematical modeling is playing a key role in many different research fields. In
the context of system biology, mathematical models and their associated computer …

We perform a general optimization of the parameters in the multilevel Monte Carlo (MLMC)
discretization hierarchy based on uniform discretization methods with general approximation …

The dynamics of molecular systems is an essential tool of systems biology. It helps figuring
out what is the effect of the perturbation of a system, or what is the best dose for a drug or …

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) …

In this work we present new scalable, information theory-based variational methods for the
efficient model reduction of high-dimensional deterministic and stochastic reaction networks …

In this work, we show that uniform integrability is not a necessary condition for central limit
theorems (CLT) to hold for normalized multilevel Monte Carlo (MLMC) estimators and we …

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 …

In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of
stochastic reaction networks characterized by having simultaneously fast and slow reaction …