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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper we consider Bayesian parameter inference associated to a classof partially observed stochastic differential equations (SDE) driven by jumpprocesses. Such type of models can be routinely found in applications, of whichwe focus upon the case of neuroscience. The data are assumed to be observedregularly in time and driven by the SDE model with unknown parameters. Inpractice the SDE may not have an analytically tractable solution and this leadsnaturally to a time-discretization. We adapt the multilevel Markov chain MonteCarlo method of [11], which works with a hierarchy of time discretizations andshow empirically and theoretically that this is preferable to using one singletime discretization. The improvement is in terms of the computational costneeded to obtain a pre-specified numerical error. Our approach is illustratedon models that are found in neuroscience.

Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience