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

In this paper we present a new approach to control variates for improvingcomputational efficiency of Ensemble Monte Carlo. We present the approach usingsimulation of paths of a time-dependent nonlinear stochastic equation. The coreidea is to extract information at one or more nominal model parameters and usethis information to gain estimation efficiency at neighboring parameters. Thisidea is the basis of a general strategy, called DataBase Monte Carlo (DBMC),for improving efficiency of Monte Carlo. In this paper we describe how thisstrategy can be implemented using the variance reduction technique of ControlVariates (CV). We show that, once an initial setup cost for extractinginformation is incurred, this approach can lead to significant gains incomputational efficiency. The initial setup cost is justified in projects thatrequire a large number of estimations or in those that are to be performedunder real-time constraints.

A Control Variate Approach for Improving Efficiency of Ensemble Monte Carlo