arXiv subjects

Evan Baker

Publications and source records attributed to Evan Baker.

4 recordsLinked to original sources

arXiv2020

Analyzing Stochastic Computer Models: A Review with Opportunities

In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models -- providing a catalogue of statistical methods for practitioners, an introductory view for statisticians (whether familiar with deterministic computer models or not), and an emphasis on open questions of relevance to practitioners and statisticians. Gaussian process surrogate models take center stage in this review, and these, along with several extensions needed for stochastic settings, are explained. The basic issues of designing a stochastic computer experiment and calibrating a stochastic computer model are prominent in the discussion. Instructive examples, with data and code, are used to describe the implementation of, and results from, various methods.

stat.ME↗

arXiv2020

Future Proofing a Building Design Using History Matching Inspired Level-Set Techniques

History Matching is a technique used to calibrate complex computer models, that is, finding the input settings which lead to the simulated output matching up with real world observations. Key to this technique is the construction of emulators, which provide fast probabilistic predictions of future simulations. In this work, we adapt the History Matching framework to tackle the problem of level set estimation, that is, finding input settings where the output is below (or above) some threshold. The developed methodology is heavily motivated by a specific case study: how can one design a building that will be sufficiently protected against overheating and sufficiently energy efficient, whilst considering the expected increases in temperature due to climate change? We successfully manage to address this - greatly reducing a large initial set of candidate building designs down to a small set of acceptable potential buildings.

stat.AP↗

arXiv2019

Diagnostics for Stochastic Gaussian Process Emulators

Computer models, also known as simulators, can be computationally expensive to run, and for this reason statistical surrogates, known as emulators, are often used. Any statistical model, including an emulator, should be validated before being used, otherwise resulting decisions can be misguided. We discuss how current methods for validating Gaussian process emulators of deterministic models are insufficient for emulators of stochastic computer models and develop a framework for diagnosing problems in stochastic emulators. These diagnostics are based on independently validating the mean and variance predictions using out-of-sample, replicated, simulator runs. We then also use a building performance simulator as a case study example.

stat.ME↗

arXiv2019

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

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.

stat.ME↗