A Control Variate Approach for Improving Efficiency of Ensemble Monte Carlo

TL;DR

This paper introduces a novel control variate method called DataBase Monte Carlo (DBMC) that leverages information from a nominal model to enhance the efficiency of ensemble Monte Carlo simulations, especially in large-scale or real-time scenarios.

Contribution

The paper proposes a new variance reduction strategy, DBMC, that uses information from a baseline model to improve Monte Carlo efficiency across multiple parameter estimations.

Findings

Significant computational gains after initial setup

Effective for large numbers of estimations or real-time applications

Applicable to nonlinear stochastic equations

Abstract

In this paper we present a new approach to control variates for improving computational efficiency of Ensemble Monte Carlo. We present the approach using simulation of paths of a time-dependent nonlinear stochastic equation. The core idea is to extract information at one or more nominal model parameters and use this information to gain estimation efficiency at neighboring parameters. This idea is the basis of a general strategy, called DataBase Monte Carlo (DBMC), for improving efficiency of Monte Carlo. In this paper we describe how this strategy can be implemented using the variance reduction technique of Control Variates (CV). We show that, once an initial setup cost for extracting information is incurred, this approach can lead to significant gains in computational efficiency. The initial setup cost is justified in projects that require a large number of estimations or in those that are to be performed under real-time constraints.