March 2023
Intermediate to advanced
243 pages
6h 14m
English
Content preview from Bayesian Optimization: Theory and Practice Using PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Index
A
Acquisition functions
Adam optimizer
AdditiveKernel() function
Airline count dataset
Airline passenger count
Analytic EI in BoTorch
best_value
ExpectedImprovement class
GP surrogate with optimized hyperparameters
Hartmann function
inner optimization routine
optimization
to() method
Approximate dynamic programming (ADP) methods
Automatic differentiation
Ax platform
B
backward() function
batch_initial_conditions variable
Bayesian decision theory
Bellman’s principle of optimality
multi-step lookahead policy
optimal policy
utility-driven optimization
Bayesian formula
Bayesian inference
actual observation
aims
Bayesian formula
Bayes’ rule
Bayes’ theorem
conjugate
denominator
normal distribution
observation model
posterior distribution
prior distribution
prior uniform distribution ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.