English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many biochemical systems appearing in applications have a multiscalestructure so that they converge to piecewise deterministic Markov processes ina thermodynamic limit. The statistics of the piecewise deterministic processcan be obtained much more efficiently than those of the exact process. Weexplore the possibility of coupling sample paths of the exact model to thepiecewise deterministic process in order to reduce the variance of theirdifference. We then apply this coupling to reduce the computational complexityof a Monte Carlo estimator. In addition to rigorous results concerning theasymptotic computational complexity of the Monte Carlo estimator, numericalsimulations are performed on some simple biological models confirming thatcomputational gains are made.

Coupling sample paths to the partial thermodynamic limit in stochastic chemical reaction networks