# Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available

## TL;DR

This paper introduces a method that leverages deterministic approximations to improve the construction of emulators for stochastic computer models, reducing the computational effort needed for analysis.

## Contribution

The paper proposes a novel approach that uses deterministic approximations to enhance stochastic emulator construction, demonstrated through various examples and real-world models.

## Key findings

- Improved emulator accuracy for stochastic models.
- Reduced number of model runs needed for effective emulation.
- Applicable to diverse fields like epidemiology and building performance.

## Abstract

The analysis of computer models can be aided by the construction of surrogate models, or emulators, that statistically model the numerical computer model. Increasingly, computer models are becoming stochastic, yielding different outputs each time they are run, even if the same input values are used. Stochastic computer models are more difficult to analyse and more difficult to emulate - often requiring substantially more computer model runs to fit. We present a method of using deterministic approximations of the computer model to better construct an emulator. The method is applied to numerous toy examples, as well as an idealistic epidemiology model, and a model from the building performance field.