With the recent rising application of mathematical models in the field of computational
systems biology, the interest in sensitivity analysis methods had increased. The stochastic …

Monte Carlo estimation in plays a crucial role in stochastic reaction networks. However,
reducing the statistical uncertainty of the corresponding estimators requires sampling a large …

This work considers the method of uniformization for continuous-time Markov chains in the
context of chemical reaction networks. Previous work in the literature has shown that …

Abstract Multilevel Monte Carlo (MLMC) methods aim to speed up computation of statistics
from dynamical simulations. MLMC is easy to implement and is sometimes very effective, but …

Stochastic simulation is a widely used method for estimating quantities in models of
chemical reaction networks where uncertainty plays a crucial role. However, reducing the …

We investigate the computational challenge of improving the accuracy of the stochastic
simulation estimation by inducing negative correlation through the anticorrelated variance …

Markovian population models are a powerful paradigm to describe processes of
stochastically interacting agents. Their dynamics is given by a continuous-time Markov …

On April 19, 2017, sad news hit all of us in the research communities of computational
biology, stochastic simulation and applied physics. Our beloved friend, Dan T. Gillespie, a …

The interpretation of sampling data plays a crucial role in policy response to the spread of a
disease during an epidemic, such as the COVID-19 epidemic of 2020. However, this is a …

In recent decades stochastic models have become an indispensable tool when analysing
quantitative biological data, which are often subject to noise, both from intrinsic and extrinsic …