Historical perspective -- Four motivating datasets -- Steepest ascent and ridge analysis -- Space-filling design -- Gaussian process regression -- Model-based design for GPs -- Optimization -- Calibration and sensitivity -- GP fidelity and scale -- Heteroskedasticity. "Surrogat…
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"Surrogates: a graduate textbook, or professional handbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), design of experiments, and optimization. Experimentation through simulation, "human out-of-t…
"Surrogates: a graduate textbook, or professional handbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), design of experiments, and optimization. Experimentation through simulation, "human out-of-t…
Résumé : With a focus on practical applications, with relevant methods supported by fundamental analysis, this book details methods of nonlinear regression, computational algorithms, model validation, interpretation of residuals, and useful experimental design. --
With a focus on practical applications, with relevant methods supported by fundamental analysis, this book details methods of nonlinear regression, computational algorithms, model validation, interpretation of residuals, and useful experimental design. --
Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineeri…
Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineeri…
With a focus on practical applications, with relevant methods supported by fundamental analysis, this book details methods of nonlinear regression, computational algorithms, model validation, interpretation of residuals, and useful experimental design. --
"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior…
"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior…
"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior…
"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior…
"This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a s…