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Solving an Inverse Problem for Time-Series-Valued Computer Simulators via Multiple Contour Estimation
Computer simulators are often used as a substitute of complex real-life phenomena, which are either expensive or infeasible to experiment with. This...
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Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator
Computer simulators are widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modelling, and...
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Statistical applications of contrastive learning
The likelihood function plays a crucial role in statistical inference and experimental design. However, it is computationally intractable for several...
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Computer Experiments
Computer experiments are integrated in modern product and service development activities. Technology is providing advanced digital platforms for...
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Improving Gaussian Process Emulators with Boundary Information
Gaussian process (GP) models are widely used as emulators of time-consuming deterministic simulators, which are mostly computer codes that solve...
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Basic Tools and Principles of Process Control
Competitive pressures are forcing many management teams to focus on process control and process improvement, as an alternative to screening and...
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Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms
Global sensitivity analysis aims to quantify the importance of model input variables for a model response. We highlight the role sensitivity analysis...
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The Evolution of Dynamic Gaussian Process Model with Applications to Malaria Vaccine Coverage Prediction
Gaussian process (GP)-based statistical surrogates are popular, inexpensive substitutes for emulating the outputs of expensive computer models that...
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Bayes Linear Emulation of Simulated Crop Yield
The analysis of the output from a large-scale computer simulation experiment can pose a challenging problem in terms of size and computation. We...
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Batch sequential adaptive designs for global optimization
Efficient global optimization (EGO) is one of the most popular sequential adaptive design (SAD) methods for expensive black-box optimization...
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Designing and Analyzing a Simulation
I noted in Chap. 7 that there are two general application domains for simulations: scientific research and...
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Introduction to Simulations
Simulations comprise the latest stage in what I will refer to as the Science-Technology Revolutionary Period. This is the result of a gradual but...
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Towards Calculating the Resilience of an Urban Transport Network Under Attack
In this article we present a methodology to calculate the resilience of a simulated cyber-physical Urban Transport Network (UTN) under attack. The...
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Bayesian Inference for Simulator Output
In Chap. 3 the correlation and precision parameters are completely unknown for the process model...
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Applications: Tactical and Strategic Scale Views
This chapter continues the examples of the melding of predictive and simulation analytics with a focus on a tactical and strategic scale view. The...
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Stochastic Process Models for Describing Computer Simulator Output
Recall from Chap. 1 that...
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Stochastics
When fitting mechanistic models to data, we have to consider carefully the relationship between the nature of the data versus the nature of the model...
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A Higher-Order Singular Value Decomposition Tensor Emulator for Spatiotemporal Simulators
We introduce methodology to construct an emulator for environmental and ecological spatiotemporal processes that uses the higher-order singular value...
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Decisions, Information, and Data
Know your audience is a well-known advice often quoted in public speaking, effective presentation, or creative writing courses. You are then taught...
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Approximate Bayesian Inference for Smoking Habit Dynamics in Tuscany
Smoking is a major risk factor for lung cancer, as well as for many other chronic diseases, and understanding smoking habits is essential to evaluate...