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3 - modeling with gaussian processes
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C - gradients
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11 - extensions and related settings
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1 - Introduction
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Frontmatter
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Preface
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9 - implementation
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B - methods for approximate bayesian inference
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10 - theoretical analysis
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8 - computing policies with gaussian processes
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12 - a brief history of bayesian optimization
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2 - gaussian processes
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A - the gaussian distribution
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5 - decision theory for optimization
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6 - utility functions for optimization
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4 - model assessment, selection, and averaging
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7 - common bayesian optimization policies
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Contents
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references
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Index
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